├── submission.zip ├── Project-MarketSegments.pdf ├── Paper.AttributedCommunity.Detection.pdf ├── submission ├── communities_0.txt ├── communities_1.txt ├── communities_5.txt ├── sac1.py └── data │ └── fb_caltech_small_attrlist.csv ├── README.md ├── evaluation.R ├── marketSegment.py ├── .Rhistory └── data ├── fb_caltech_small_attrlist.csv └── fb_caltech_small_edgelist.txt /submission.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/triandicAnt/FacebookCommunityDetection/HEAD/submission.zip -------------------------------------------------------------------------------- /Project-MarketSegments.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/triandicAnt/FacebookCommunityDetection/HEAD/Project-MarketSegments.pdf -------------------------------------------------------------------------------- /Paper.AttributedCommunity.Detection.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/triandicAnt/FacebookCommunityDetection/HEAD/Paper.AttributedCommunity.Detection.pdf -------------------------------------------------------------------------------- /submission/communities_0.txt: -------------------------------------------------------------------------------- 1 | 4,29,5,67,6,24,10,47,12,108 2 | 8,18,9,51,91 3 | 20,79,34,244 4 | 7,210,13,96,15,19,37,16,98,21,135,22,161,35,66,75,103,94,181,107,178,119,136,193,182,184 5 | 50,145,113,125,162,254 6 | 11,117,38,42,90,52,57,86,298,93,191,112,203,118,241,163,185,165,189,199,218 7 | 74,128,155,206,235,231,290 8 | 54 9 | 242 10 | 71,78,84,238,101,138,123,253,126,153,134,177,169,221,292,217,277,293,257,320 11 | 17,168,25,70,32,269,36,110,65,176,77,133,150,234,95,106,237,124,140,141,187,146,166,208,256,317,252,266,267 12 | 1,73,28,44,33,68,43,152,59,62,61,64,76,160,89,211,100,154,105,281,120,147,158,130,207,139,183,173,236,202,229,274,308,310,280,300 13 | 41,296,48,179,49,248,72,157,97,129,225,121,261,306,151,200,196,313,198,276,215,224,222,243,263,275,272,304,278,312,314,283,318,321 14 | 2,180,14,197,26,63,31,60,39,56,40,83,46,109,58,137,111,170,114,131,149,240,156,295,172,294,305,174,307,311,186,227,259,232,303,273,319 15 | 27,88,30,55,45,205,80,171,271,81,247,99,175,104,115,122,142,164,245,192,230,194,315,323,219,285,220,239,264,223,284,228,255,291,251,301,309,270,287,297,316 16 | 0,167,3,23,53,250,268,69,159,82,85,262,87,204,209,92,216,102,116,127,144,132,143,249,148,265,188,212,190,213,195,226,258,201,279,214,282,289,233,260,246,288,286,299,302,322 17 | -------------------------------------------------------------------------------- /submission/communities_1.txt: -------------------------------------------------------------------------------- 1 | 0, 2 | 1, 3 | 3, 4 | 5, 5 | 7, 6 | 8, 7 | 9, 8 | 10, 9 | 12, 10 | 14, 11 | 25, 12 | 28, 13 | 35, 14 | 50, 15 | 71, 16 | 86, 17 | 92, 18 | 107, 19 | 109, 20 | 110, 21 | 118, 22 | 122, 23 | 131, 24 | 139, 25 | 173, 26 | 177, 27 | 183, 28 | 184, 29 | 186, 30 | 188, 31 | 198, 32 | 202, 33 | 203, 34 | 206, 35 | 223, 36 | 230, 37 | 235, 38 | 256, 39 | 261, 40 | 270, 41 | 275, 42 | 279, 43 | 281, 44 | 283, 45 | 296, 46 | 301, 47 | 304, 48 | 306, 49 | 315,2,4,6,11,13,15,16,17,18,19,20,21,22,23,24,26,27,29,30,31,32,33,34,36,37,38,39,40,41,42,43,44,45,46,47,48,49,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,72,73,74,75,76,77,78,79,80,81,82,83,84,85,87,88,89,90,91,93,94,95,96,97,98,99,100,101,102,103,104,105,106,108,111,112,113,114,115,116,117,119,120,121,123,124,125,126,127,128,129,130,132,133,134,135,136,137,138,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,174,175,176,178,179,180,181,182,185,187,189,190,191,192,193,194,195,196,197,199,200,201,204,205,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,224,225,226,227,228,229,231,232,233,234,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,257,258,259,260,262,263,264,265,266,267,268,269,271,272,273,274,276,277,278,280,282,284,285,286,287,288,289,290,291,292,293,294,295,297,298,299,300,302,303,305,307,308,309,310,311,312,313,314, -------------------------------------------------------------------------------- /submission/communities_5.txt: -------------------------------------------------------------------------------- 1 | 0, 2 | 2, 3 | 3, 4 | 4, 5 | 5, 6 | 6, 7 | 7, 8 | 8, 9 | 9, 10 | 10, 11 | 11, 12 | 12, 13 | 13, 14 | 14, 15 | 15, 16 | 16, 17 | 17, 18 | 18, 19 | 19, 20 | 20, 21 | 21, 22 | 22, 23 | 23, 24 | 24, 25 | 25, 26 | 26, 27 | 27, 28 | 28, 29 | 29, 30 | 30, 31 | 31, 32 | 32, 33 | 33, 34 | 34, 35 | 35, 36 | 36, 37 | 37, 38 | 38, 39 | 39, 40 | 40, 41 | 41, 42 | 42, 43 | 43, 44 | 44, 45 | 45, 46 | 46, 47 | 47, 48 | 48, 49 | 49, 50 | 50, 51 | 51, 52 | 52, 53 | 53, 54 | 54, 55 | 55, 56 | 56, 57 | 57, 58 | 58, 59 | 59, 60 | 60, 61 | 61, 62 | 62, 63 | 63, 64 | 64, 65 | 65, 66 | 69, 67 | 86, 68 | 87, 69 | 113, 70 | 120, 71 | 121, 72 | 147, 73 | 148, 74 | 151, 75 | 153, 76 | 154, 77 | 165, 78 | 168, 79 | 169, 80 | 173, 81 | 174, 82 | 186, 83 | 188, 84 | 190, 85 | 197, 86 | 199, 87 | 203, 88 | 208, 89 | 209, 90 | 214, 91 | 216, 92 | 223, 93 | 227, 94 | 229, 95 | 234, 96 | 235, 97 | 242, 98 | 243, 99 | 245, 100 | 248, 101 | 250, 102 | 259, 103 | 260, 104 | 263, 105 | 266, 106 | 268, 107 | 269, 108 | 270, 109 | 271, 110 | 274, 111 | 278, 112 | 281, 113 | 283, 114 | 284, 115 | 287, 116 | 289, 117 | 290, 118 | 292, 119 | 293, 120 | 295, 121 | 296, 122 | 297, 123 | 298, 124 | 301, 125 | 304, 126 | 305, 127 | 309, 128 | 311, 129 | 318,1,66,67,68,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,114,115,116,117,118,119,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,149,150,152,155,156,157,158,159,160,161,162,163,164,166,167,170,171,172,175,176,177,178,179,180,181,182,183,184,185,187,189,191,192,193,194,195,196,198,200,201,202,204,205,206,207,210,211,212,213,215,217,218,219,220,221,222,224,225,226,228,230,231,232,233,236,237,238,239,240,241,244,246,247,249,251,252,253,254,255,256,257,258,261,262,264,265,267,272,273,275,276,277,279,280,282,285,286,288,291,294,299,300,302,303,306,307,308,310,312,313,314,315,316,317, -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Community Detection( Market Segmentation) 2 | Find market segments in social network data of Facebook users. 3 | 4 | ## Objective: 5 | * Data consisted of users as nodes and their relationship between friends as their edges. 6 | * The algorithm detected communities in attributed graph data based on the structural, demographic (age, sex, ethnicity, education) and attribute similarities using Python's iGraph module. 7 | * The found segments were evaluated via influence propagation. 8 | * In influence propagation an entity in the segment is influenced and how fast the influence propagates over the entire network is evaluated. 9 | * The faster the influence propagates, the better is the segment. 10 | 11 | ## Data sets: 12 | http://masonporter.blogspot.in/2011/02/facebook100-data-set.html 13 | 14 | ## Requirements: 15 | * igraph : http://igraph.org/ 16 | * scipy 17 | 18 | ## Steps to Run: 19 | * Load Facebook data and find market segments: 20 | ```python 21 | python marketSegment.py 22 | ``` 23 | * Evaluate the results: 24 | ```R 25 | evaluation.R 26 | ``` 27 | ## Results: 28 | Node Ids grouped together: 29 | ``` 30 | 4,29,5,67,6,24,10,47,12,108 31 | 8,18,9,51,91 32 | 20,79,34,244 33 | 7,210,13,96,15,19,37,16,98,21,135,22,161,35,66,75,103,94,181,107,178,119,136,193,182,184 34 | 50,145,113,125,162,254 35 | 11,117,38,42,90,52,57,86,298,93,191,112,203,118,241,163,185,165,189,199,218 36 | 74,128,155,206,235,231,290 37 | 54 38 | 242 39 | 71,78,84,238,101,138,123,253,126,153,134,177,169,221,292,217,277,293,257,320 40 | 17,168,25,70,32,269,36,110,65,176,77,133,150,234,95,106,237,124,140,141,187,146,166,208,256,317,252,266,267 41 | 1,73,28,44,33,68,43,152,59,62,61,64,76,160,89,211,100,154,105,281,120,147,158,130,207,139,183,173,236,202,229,274,308,310,280,300 42 | 41,296,48,179,49,248,72,157,97,129,225,121,261,306,151,200,196,313,198,276,215,224,222,243,263,275,272,304,278,312,314,283,318,321 43 | 2,180,14,197,26,63,31,60,39,56,40,83,46,109,58,137,111,170,114,131,149,240,156,295,172,294,305,174,307,311,186,227,259,232,303,273,319 44 | 27,88,30,55,45,205,80,171,271,81,247,99,175,104,115,122,142,164,245,192,230,194,315,323,219,285,220,239,264,223,284,228,255,291,251,301,309,270,287,297,316 45 | 0,167,3,23,53,250,268,69,159,82,85,262,87,204,209,92,216,102,116,127,144,132,143,249,148,265,188,212,190,213,195,226,258,201,279,214,282,289,233,260,246,288,286,299,302,322 46 | ``` 47 | -------------------------------------------------------------------------------- /evaluation.R: -------------------------------------------------------------------------------- 1 | library(igraph) 2 | #install.packages("akmeans") 3 | library("akmeans") 4 | 5 | ## Influence Propagation 6 | # Arguments: 7 | # Graph = Structural Graph, Igraph Instance 8 | # Comm = Communiity/Clusters 9 | # Beta = Influence Propagation Rate 10 | 11 | influence_propagation <- function(graph, comm, beta=0.75){ 12 | output = c() 13 | v = vcount(graph) 14 | nodes <- data.frame(1:v) 15 | colnames(nodes) <- c("Id") 16 | nodes$comm = comm 17 | nodes$color = "green" 18 | 19 | influencers = c() 20 | for(k in unique(comm)){ 21 | influencer = sample(nodes[which(nodes$comm %in% k), c("Id")],1) 22 | influencers = append(influencers, influencer) 23 | } 24 | 25 | nodes[nodes$Id %in% influencers, c("color")] <- "red" 26 | new_influencers = influencers 27 | total_influencers = length(influencers) 28 | 29 | timestep = 1 30 | old_influencers = 0 31 | ratio = total_influencers/v 32 | 33 | cat("Time Step = " , timestep,", Total Influencers = ", total_influencers, ", Network Coverage = " , ratio ,"\n") 34 | output = data.frame(timestep, total_influencers) 35 | 36 | timestep = timestep + 1 37 | alpha = 0.7 38 | 39 | #while(total_influencers - old_influencers > 0 && ratio < 1){ 40 | for(i in 1:15){ 41 | old_influencers = total_influencers 42 | influencers = new_influencers 43 | for(i in influencers){ 44 | nbs = neighbors(graph, i) 45 | ## Selection if the propagation is inside community or outside 46 | flag = "inside" 47 | if(sample(1:100, 1) <= alpha*100){ 48 | 49 | candidates = nodes[nodes$comm == nodes[i, c("comm")] & nodes$color == "green", c("Id")] 50 | candidates = intersect(candidates, nbs) 51 | ids <- c() 52 | for(j in candidates){ 53 | if(j %in% nbs && sample(1:100, 1) <= beta * 100){ # Stochastically determining if node will get infected, using beta transmission probability 54 | ids <- append(ids, j) 55 | } 56 | } 57 | }else{ 58 | candidates = nodes[nodes$comm != nodes[i, c("comm")] & nodes$color == "green", c("Id")] 59 | candidates = intersect(candidates, nbs) 60 | ids <- c() 61 | for(j in candidates){ 62 | if(sample(1:100, 1) <= beta * 100){ # Stochastically determining if node will get infected, using beta transmission probability 63 | ids <- append(ids, j) 64 | } 65 | } 66 | } 67 | nodes[which(nodes$Id %in% ids), c("color")] <- "red" 68 | total_influencers = nrow(nodes[nodes$color == "red",]) 69 | new_influencers = append(new_influencers, unique(ids)) 70 | } 71 | ratio = nrow(nodes[nodes$color == "red",])/v 72 | cat("Time Step = " ,timestep,", Total Influencers = ", total_influencers, ", Network Coverage = ", ratio ,"\n") 73 | output = rbind(output, data.frame(timestep, total_influencers)) 74 | timestep = timestep + 1 75 | } 76 | output 77 | } 78 | 79 | ## Influence Propagation Example 80 | 81 | # ===== Reading the graph ====== # 82 | g <- read.graph(file="data/fb_caltech_small_edgelist.txt", format = c("edgelist")) 83 | 84 | # ===== Read communities file ==== # 85 | comm = integer(vcount(g)) 86 | 87 | conn <- file("communities.txt", open="r") 88 | lines <-readLines(conn) 89 | for (i in 1:length(lines)){ 90 | verts = unlist(strsplit(lines[i], ",")) 91 | verts = as.integer(verts) + 1 # R indexes start at 1 92 | comm[verts] = i 93 | } 94 | close(conn) 95 | 96 | 97 | # ===== Clusters From Adaptive K-means ===== # 98 | attrData <- read.csv("data/fb_caltech_small_attrlist.csv") 99 | akm = akmeans(attrData, d.metric=2, ths3=.4, mode=3, 100 | min.k=length(unique(comm)), max.k=length(unique(comm))) ## cosine distance based 101 | 102 | # ===== Influence Propagation Using adaptive k-means clusters ===== # 103 | op1 = influence_propagation(graph=g, comm = akm$cluster) 104 | 105 | # ===== Influence Propagation Using SAC-1 communities ===== # 106 | op2 = influence_propagation(graph=g, comm = comm) 107 | 108 | print("here") 109 | 110 | 111 | ## Plot for the timestep comparison 112 | tsteps = as.vector(op1$timestep) 113 | tsteps = append(tsteps, as.vector(op2$timestep)) 114 | plot(op1, type="b", col=2, pch=20, ylim = c(0, vcount(g)), xlim = c(1, max(tsteps)), main = "Influence Propagation") 115 | points(op2, type="b", col=3, pch=20) 116 | legend("bottomright", c("Adaptive-Kmeans","SAC-1"), col=2:3, pch=c(20,20)) 117 | -------------------------------------------------------------------------------- /submission/sac1.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | import igraph as ig 4 | from igraph import * 5 | from itertools import izip 6 | import csv 7 | import numpy as np 8 | import time 9 | from scipy import spatial 10 | import scipy 11 | from sklearn.metrics.pairwise import cosine_similarity 12 | import itertools 13 | 14 | g = Graph.Read_Edgelist('data/fb_caltech_small_edgelist.txt', directed=False) 15 | # g= ig.Graph.Read_Ncol('./data/fb_caltech_small_edgelist.txt',directed=False) 16 | 17 | arg_list = sys.argv 18 | if len(arg_list)<=0 or len(arg_list)>2: 19 | print "Invalid number of arguments." 20 | sys.exit(0) 21 | global alpha 22 | alpha = int(arg_list[1]) 23 | # alpha = 0 24 | attrList = [] 25 | with open("data/fb_caltech_small_attrlist.csv") as fileCSV: 26 | reader = csv.reader(fileCSV) 27 | attrList = list(reader)[1:] 28 | 29 | edgeList = [] 30 | edgeList = g.get_edgelist() 31 | 32 | vertices = g.vcount() 33 | 34 | membership = [] 35 | for v in range(0,g.vcount()): 36 | membership.append(v) 37 | 38 | 39 | communities = list(Clustering(membership)) 40 | 41 | def simplifyMembership(membership): 42 | dict = {} 43 | ml = [] 44 | i=0 45 | for j in membership: 46 | if j in dict.keys(): 47 | ml.append(dict[j]) 48 | else: 49 | ml.append(j) 50 | dict[j] = i 51 | i+=1 52 | return ml 53 | 54 | def refreshCommunities(c1,c2): 55 | r = [] 56 | for i in c2: 57 | temp = [] 58 | for j in i: 59 | temp.append(c1[j]) 60 | r.append(temp) 61 | return r 62 | 63 | def getSimMatrix (): 64 | # sim = np.zeros(shape=(len(attrList),len(attrList))) 65 | sim = [[0]*len(attrList)]*len(attrList) 66 | for i in range(len(attrList)): 67 | for j in range(len(attrList)): 68 | # print cosine_similarity(attrList[i],attrList[j])[0][0] 69 | sim[i][j] = (cosine_similarity(attrList[i],attrList[j])[0][0]).reshape(1,-1) 70 | return sim 71 | 72 | def getSimilarityMatrix(): 73 | sim = [] 74 | for i in range(0,vertices): 75 | temp = [] 76 | for j in range(0,vertices): 77 | # temp.append(cosineSimilarity(dict[i],dict[j])) 78 | temp.append(spatial.distance.cosine(np.array(attrList[i],dtype=int), np.array(attrList[j],dtype=int))) 79 | sim.append(temp) 80 | return sim 81 | 82 | def updateSimMatrix (newNodes, sim): 83 | newSim = [[0]*len(newNodes)]*len(newNodes) 84 | for n1,i in enumerate(newNodes): 85 | for n2 ,j in enumerate(newNodes): 86 | temp =0 87 | count = 0 88 | for k in i: 89 | for l in j: 90 | temp+=sim[k][l] 91 | count+=1 92 | newSim[n1][n2] = temp 93 | return newSim 94 | 95 | global sim 96 | sim = getSimMatrix() 97 | copy_sm = sim 98 | 99 | def phase1(membership): 100 | ver = len(set(membership)) 101 | for k in range(15): 102 | print k 103 | for i in range(ver): 104 | m_g = 0.0 105 | old_newman = g.modularity(membership) 106 | del_newman = 0.0 107 | max = -1 108 | temp = membership[i] 109 | for j in range(ver): 110 | if (j!=i) and membership[i]!=membership[j]: 111 | membership[i] = membership[j] 112 | new_newman = g.modularity(membership) 113 | del_newman = new_newman - old_newman 114 | delattr = 0.0 115 | x = 0 116 | for k in membership: 117 | if(k==membership[j]): 118 | x+=1 119 | for v, e in enumerate(membership): 120 | if(membership[j]==e): 121 | delattr+=sim[i][v] 122 | delattr/=x**2 123 | delattr/=len(set(membership)) 124 | delQ = alpha*del_newman+(1-alpha)*delattr 125 | if delQ>m_g: 126 | m_g = delQ 127 | max = j 128 | membership[i] = temp 129 | if m_g >0 and max >1: 130 | membership[i] = membership[max] 131 | return membership 132 | 133 | def phase2(c, membership): 134 | membership = simplifyMembership(membership) 135 | g.contract_vertices(membership) 136 | g.simplify(multiple = True) 137 | c1 = list(Clustering(membership)) 138 | c = refreshCommunities(c,c1) 139 | 140 | sim = updateSimMatrix(c1,copy_sm) 141 | vertices = g.vcount() 142 | membership = [] 143 | for i in range(vertices): 144 | membership.append(i) 145 | # membership = [j for j in ] 146 | return membership 147 | 148 | # def setDefaults(): 149 | membership = phase1(membership) 150 | membership = phase2(communities,membership) 151 | membership = phase1(membership) 152 | membership = simplifyMembership(membership) 153 | communities = list(Clustering(membership)) 154 | file = open("communities.txt", "w+") 155 | for l in list(g.clusters()): 156 | if len(g.clusters())!=0: 157 | file.write("\n".join(str(x)+"," for x in l)) 158 | file.close() 159 | 160 | def main(): 161 | # sim = getSimilarityMatrix() 162 | # print sim 163 | if __name__ == "__main__": main() 164 | 165 | -------------------------------------------------------------------------------- /marketSegment.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | import igraph as ig 4 | from igraph import * 5 | from itertools import izip 6 | import csv 7 | import numpy as np 8 | import time 9 | from scipy import spatial 10 | 11 | alpha = 0 12 | g= ig.Graph.Read_Ncol('./data/fb_caltech_small_edgelist.txt',directed=False) 13 | #ig.plot(g) 14 | edgeList = [] 15 | #print summary(g) 16 | for e in g.es: 17 | edgeList.append(e.tuple) 18 | #print edgeList 19 | 20 | vertices = [] 21 | dict = {} 22 | for v in g.vs: 23 | vertices.append(int(v["name"])) 24 | dict[int(v["name"])] = v 25 | # print sorted(vertices) 26 | 27 | attrList = [] 28 | i=0 29 | file = open('./data/fb_caltech_small_attrlist.csv') 30 | reader = csv.reader(file) 31 | header = reader.next() 32 | noOfAttrs = header 33 | # print header 34 | for row in reader: 35 | attrList.append(row) 36 | for attrName, attrVal in izip(header,row): 37 | g.vs[i][attrName] = attrVal 38 | i=i+1 39 | # print attrList 40 | # print g.vs[1] 41 | 42 | # calculate cosine similarity of graph matrix 43 | # Cosine Similarity (d1, d2) = Dot product(d1, d2) / ||d1|| * ||d2|| 44 | # ref : https://janav.wordpress.com/2013/10/27/tf-idf-and-cosine-similarity/ 45 | sim = [] 46 | def getSimilarityMatrix(): 47 | for i in range(0,len(vertices)): 48 | temp = [] 49 | for j in range(0,len(vertices)): 50 | # temp.append(cosineSimilarity(dict[i],dict[j])) 51 | temp.append(spatial.distance.cosine(np.array(attrList[i],dtype=int), np.array(attrList[j],dtype=int))) 52 | sim.append(temp) 53 | 54 | def getDotProduct(v1,v2): 55 | sum =0 56 | for i in noOfAttrs: 57 | sum = sum + int(v1[i])*int(v2[i]) 58 | return sum 59 | 60 | def getModOfVertexAttr(v): 61 | sum = 0 62 | for i in noOfAttrs: 63 | sum = sum + math.pow(int(v[i]),2) 64 | return sum 65 | 66 | def cosineSimilarity(v1,v2): 67 | # dotProduct = getDotProduct(v1,v2) 68 | # modv1 = getModOfVertexAttr(v1) 69 | # modv2 = getModOfVertexAttr(v2) 70 | dot = 0 71 | mod1 = 0 72 | mod2 = 0 73 | for i in noOfAttrs: 74 | mod1 = mod1 + math.pow(int(v1[i]),2) 75 | mod2 = mod2 + math.pow(int(v2[i]),2) 76 | dot = dot + int(v1[i])*int(v2[i]) 77 | consineSim = dot/(mod1*mod2) 78 | return consineSim 79 | def findSimilarity(idx,mem,val): 80 | matchings_indices = [ i for i, x in enumerate(mem) if x == val ] 81 | print(matchings_indices) 82 | l = len(matchings_indices) 83 | sum = 0 84 | for n in matchings_indices: 85 | sum = sum + sim[idx][matchings_indices[n]] 86 | return sum 87 | 88 | # calculate QNewman 89 | def getQAttrDiff(idx): 90 | qarrt = 0 91 | for i in vertices: 92 | qarrt = qarrt + cosineSimilarity(dict[idx],dict[i]) 93 | return qarrt 94 | 95 | def delQAttrCalculate(idx, c_list): 96 | sum = 0 97 | for i in c_list: 98 | sum = sum + sim[idx][i] 99 | return sum 100 | 101 | compare = lambda a,b: len(a)==len(b) and len(a)==sum([1 for i,j in zip(a,b) if i==j]) 102 | membership = range(0,len(vertices)) 103 | communities= {node:[node] for node in vertices} 104 | def algorithm(): 105 | # print noOfAttrs 106 | # print g.get_adjlist() 107 | x = 1 108 | for m in range(0,15): 109 | x = x +1 110 | print x 111 | old_m = list(membership) 112 | print old_m 113 | for i in range(0,len(vertices)): 114 | # for i in range(0,5): 115 | QNewman_Old = g.modularity(np.array(membership)) 116 | max = -5 117 | index = i 118 | mem_ini = membership[i] 119 | # print QNewman_Old 120 | ts = time.time() 121 | # print ts 122 | cl_list = list(Clustering(membership)) 123 | for j in range(0,len(vertices)): 124 | if i!=j: 125 | if len(cl_list[membership[j]]) > 0: 126 | membership[i] = membership[j] 127 | QNewman_new = g.modularity(np.array(membership)) 128 | qarrt = delQAttrCalculate(j, cl_list[membership[j]]) 129 | qarrt = qarrt/(len(cl_list[membership[j]])*len(cl_list[membership[j]])) 130 | qarrt = qarrt/len(set(membership)) 131 | # qarrt = findSimilarity(i,membership,membership[j]) 132 | delQNew = (QNewman_new-QNewman_Old) 133 | delQAttr = alpha*delQNew + (1-alpha)*qarrt 134 | # print delQAttr 135 | if delQAttr>max: 136 | index = j 137 | max = delQAttr 138 | if max > 0: 139 | membership[i] = membership[index] 140 | else: 141 | membership[i] = mem_ini 142 | cl_list = list(Clustering(membership)) 143 | # print cl_list 144 | # print ts 145 | print membership 146 | # print membership 147 | if compare(old_m,membership) == True: 148 | print "Breaking Bad!" 149 | break 150 | d=0 151 | for x in list(Clustering(membership)): 152 | if len(x)!=0: 153 | d = d+1 154 | print x 155 | print d 156 | # a = g.clusters 157 | def phase2(): 158 | algorithm() 159 | g2 = g.contract_vertices(membership) 160 | print g2.vs 161 | 162 | def main(): 163 | getSimilarityMatrix() 164 | # print sim 165 | phase2() 166 | # display some lines 167 | 168 | if __name__ == "__main__": main() 169 | 170 | -------------------------------------------------------------------------------- /.Rhistory: -------------------------------------------------------------------------------- 1 | res <- residuals(fc) 2 | Acf(res) 3 | test <- Box.test(res, lag=4, fitdf = 0, type = "Lj") 4 | if (test["p.value"] < 0.05){ 5 | print ("Not a white noise") 6 | } else { 7 | print ("White noise") 8 | } 9 | require(fpp) 10 | data(ausbeer) 11 | beer <- window(ausbeer,start=1992) 12 | res = residuals(snaive(beer)) 13 | acf(res) 14 | require(fpp) 15 | data(ausbeer) 16 | fit <- window(ausbeer,start=1992) 17 | res = residuals(snaive(fit)) 18 | acf(res) 19 | fit <- window(ausbeer,start=1992) 20 | fit 21 | res = residuals(snaive(fit)) 22 | acf(res) 23 | Acf(res) 24 | require(fpp) 25 | data(ausbeer) 26 | fit <- window(ausbeer,start=1992) 27 | res = residuals(snaive(fit)) 28 | Acf(res) 29 | value<-Box.test(residuals(snaive(fit)), fitdf=0,type = "Lj") 30 | if (value["p.value"] < 0.05){ 31 | print ("Not a white noise.") 32 | } else { 33 | print ("White noise.") 34 | } 35 | #----------------------------- 36 | require (multcomp) 37 | install.packages("multcomp") 38 | #--------------------------- 39 | require (multcomp) 40 | attach (cholesterol) 41 | head (cholesterol) 42 | table(trt) 43 | aggregate (response, by=list(trt), FUN=mean) 44 | aggregate (response, by=list(trt), FUN=sd) 45 | fit <- aov (response ~ trt) 46 | summary(fit) 47 | # Plot group means and confidence intervals 48 | library (gplots) 49 | library (gplots) 50 | install.packages("gplots") 51 | library (gplots) 52 | plotmeans (response ~trt, xlab="Treatment", 53 | ylab = "Cholesterol Reduction", 54 | main="Means with 95% CI") 55 | # F-test is significant: 56 | # Conclusion: five treatments are not equally effective 57 | # But: it does not tell which treatments differ from one another 58 | # Use: TukeyHSD() to test all pairwise differences or 59 | # glht() for multiple mean comparisons 60 | TukeyHSD(fit) 61 | par(las=2) 62 | par (mar=c(5,8,4,2)) 63 | plot (TukeyHSD(fit)) 64 | library(multicomp) 65 | par (mar = c(5,4,6,2)) 66 | comp <- glht(fit, linfct=mcp(trt="Tukey")) 67 | plot (cld(comp, level=0.05), col="lightgrey") 68 | install.packages("multicomp") 69 | comp <- glht(fit, linfct=mcp(trt="Tukey")) 70 | plot (cld(comp, level=0.05), col="lightgrey") 71 | #----------------------------------------------- 72 | # Assumptions underlying one-way ANOVA tests: 73 | # Dependent var: is normally distributed 74 | # Dependent var: has equal variance in each trt group 75 | # ANOVA can be sensistive to outliers 76 | #----------------------------------------------- 77 | library (car) 78 | lm.fit <- lm(response ~ trt, data=cholesterol) 79 | qqPlot (lm.fit, simulate=TRUE, 80 | main="Q-Q Plot", labels=FALSE) 81 | # test equality / homegeneity of variances 82 | bartlett.test (response ~ trt, data=cholesterol) 83 | # test for outliers: NA is p-value > 1 84 | # Significant test: No indication of outliers 85 | library(car) 86 | outlierTest(fit) 87 | library(multcomp) 88 | comp <- glht(fit, linfct=mcp(trt="Tukey")) 89 | plot (cld(comp, level=0.05), col="lightgrey") 90 | #----------------------------------------------- 91 | # Assumptions underlying one-way ANOVA tests: 92 | # Dependent var: is normally distributed 93 | # Dependent var: has equal variance in each trt group 94 | # ANOVA can be sensistive to outliers 95 | #----------------------------------------------- 96 | library (car) 97 | data(package="fpp") 98 | plot(elecequip) 99 | plot(elecequip) 100 | library(fpp) 101 | plot(elecequip) 102 | ets(elecequip) 103 | #decompose using stl 104 | fit<-stl(elecequip,s.window = "periodic") 105 | plot(fit,col="green",main="Electrical equipments.") 106 | plot(fit,col="blue",main="Electrical equipments.") 107 | plot(elecequip,col="red", main="Electrical equipments.") 108 | lines(seasadj(fit), col="blue", main="seasonally adjusted elecequip") 109 | seasonal_adj<-seasadj(fit) 110 | lambda<-BoxCox.lambda(seasonal_adj) 111 | result<-BoxCox(seasonal_adj,lambda) 112 | acf(result) 113 | Box.test(result, lag=10, fitdf=0, type="Lj") 114 | ndiff<-ndiffs(result) 115 | result<- diff(result,differences=ndiff) 116 | plot(result) 117 | acf(result) 118 | Box.test(result, lag=10, fitdf=0, type="Lj") 119 | result<-auto.arima(result) 120 | Box.test(result, lag=10, fitdf=0, type="Lj") 121 | acf(result) 122 | ndiff<-ndiffs(result) 123 | lambda<-BoxCox.lambda(seasonal_adj) 124 | result<-BoxCox(seasonal_adj,lambda) 125 | # 5. Confirm if the resulting data after Step 4 is stationary. 126 | acf(result) 127 | Box.test(result, lag=10, fitdf=0, type="Lj") 128 | # 6. If the data is non-stationary, then take the first difference of the data or run the unit root test to get the proper differencing order and check whether the differencing converted non-stationary ts to a stationarity one. 129 | ndiff<-ndiffs(result) 130 | result<- diff(result,differences=ndiff) 131 | plot(result) 132 | acf(result) 133 | Box.test(result, lag=10, fitdf=0, type="Lj") 134 | # 7. Use auto.arima() on the time series data obtained after Step #6. What are the values of p, d, and q in ARIMA (p, d, q) model that the model has chosen (use summary() on the model) 135 | result<-auto.arima(result) 136 | result<-auto.arima(result,approximation=FALSE,trace=FALSE) 137 | lambda<-BoxCox.lambda(seasonal_adj) 138 | result<-BoxCox(seasonal_adj,lambda) 139 | # 5. Confirm if the resulting data after Step 4 is stationary. 140 | acf(result) 141 | Box.test(result, lag=10, fitdf=0, type="Lj") 142 | # 6. If the data is non-stationary, then take the first difference of the data or run the unit root test to get the proper differencing order and check whether the differencing converted non-stationary ts to a stationarity one. 143 | ndiff<-ndiffs(result) 144 | result<- diff(result,differences=ndiff) 145 | plot(result) 146 | acf(result) 147 | Box.test(result, lag=10, fitdf=0, type="Lj") 148 | # 7. Use auto.arima() on the time series data obtained after Step #6. What are the values of p, d, and q in ARIMA (p, d, q) model that the model has chosen (use summary() on the model) 149 | result<-auto.arima(result,approximation=FALSE,trace=FALSE) 150 | summary(auto.arima(result)) 151 | lambda<-BoxCox.lambda(seasonal_adj) 152 | result<-BoxCox(seasonal_adj,lambda) 153 | # 5. Confirm if the resulting data after Step 4 is stationary. 154 | acf(result) 155 | Box.test(result, lag=10, fitdf=0, type="Lj") 156 | # 6. If the data is non-stationary, then take the first difference of the data or run the unit root test to get the proper differencing order and check whether the differencing converted non-stationary ts to a stationarity one. 157 | # Check error/trend/seasonal of elecequip 158 | ets(elecequip) 159 | #decompose using stl 160 | fit<-stl(elecequip, s.window="periodic") 161 | plot(fit,col="blue",main="Electrical equipments.") 162 | # 3. If the data is seasonal, create and plot the seasonally adjusted data using seasadj(). 163 | plot(elecequip,col="red", main="Electrical equipments.") 164 | lines(seasadj(fit), col="blue", main="seasonally adjusted elecequip") 165 | seasonal_adj<-seasadj(fit) 166 | # 4. Apply Box-Cox transformation only if there is there is the need to stabilize the variance of the time series obtained in Step 3. 167 | lambda<-BoxCox.lambda(seasonal_adj) 168 | result<-BoxCox(seasonal_adj,lambda) 169 | # 5. Confirm if the resulting data after Step 4 is stationary. 170 | acf(result) 171 | Box.test(result, lag=10, fitdf=0, type="Lj") 172 | # 6. If the data is non-stationary, then take the first difference of the data or run the unit root test to get the proper differencing order and check whether the differencing converted non-stationary ts to a stationarity one. 173 | ndiff<-ndiffs(result) 174 | result<- diff(result,differences=ndiff) 175 | plot(result) 176 | acf(result) 177 | Box.test(result, lag=10, fitdf=0, type="Lj") 178 | # 7. Use auto.arima() on the time series data obtained after Step #6. What are the values of p, d, and q in ARIMA (p, d, q) model that the model has chosen (use summary() on the model) 179 | result<-auto.arima(result,approximation=FALSE,trace=FALSE) 180 | # to remove the Warning message: 181 | #In auto.arima(result) : 182 | # Unable to fit final model using maximum likelihood. AIC value approximated 183 | # used -- approximation=FALSE,trace=FALSE) 184 | # ref http://stats.stackexchange.com/questions/89316/warning-message-in-auto-arima 185 | summary(auto.arima(result)) 186 | summary(result) 187 | Arima(result, order=c(4,0,0)) 188 | lambda<-BoxCox.lambda(seasonal_adj) 189 | result<-BoxCox(seasonal_adj,lambda) 190 | # 5. Confirm if the resulting data after Step 4 is stationary. 191 | acf(result) 192 | Box.test(result, lag=10, fitdf=0, type="Lj") 193 | # 6. If the data is non-stationary, then take the first difference of the data or run the unit root test to get the proper differencing order and check whether the differencing converted non-stationary ts to a stationarity one. 194 | ndiff<-ndiffs(result) 195 | result<- diff(result,differences=ndiff) 196 | plot(result) 197 | acf(result) 198 | Box.test(result, lag=10, fitdf=0, type="Lj") 199 | # 7. Use auto.arima() on the time series data obtained after Step #6. What are the values of p, d, and q in ARIMA (p, d, q) model that the model has chosen (use summary() on the model) 200 | result<-auto.arima(result,approximation=FALSE,trace=FALSE) 201 | # to remove the Warning message: 202 | #In auto.arima(result) : 203 | # Unable to fit final model using maximum likelihood. AIC value approximated 204 | # used -- approximation=FALSE,trace=FALSE) 205 | # ref http://stats.stackexchange.com/questions/89316/warning-message-in-auto-arima 206 | summary(result) 207 | # 8. Generate your own ARIMA(4, 0, 0), ARIMA (3, 0,0) and ARIMA (2, 0, 0) models using the following command: 208 | #Arima(ts.data, order=c(4,0,0)) 209 | Arima(result, order=c(4,0,0)) 210 | Arima(result.data, order=c(4,0,0)) 211 | Arima(result, order=c(4,0,0)) 212 | result1<-auto.arima(result,approximation=FALSE,trace=FALSE) 213 | lambda<-BoxCox.lambda(seasonal_adj) 214 | result<-BoxCox(seasonal_adj,lambda) 215 | # 5. Confirm if the resulting data after Step 4 is stationary. 216 | acf(result) 217 | Box.test(result, lag=10, fitdf=0, type="Lj") 218 | # 6. If the data is non-stationary, then take the first difference of the data or run the unit root test to get the proper differencing order and check whether the differencing converted non-stationary ts to a stationarity one. 219 | ndiff<-ndiffs(result) 220 | result<- diff(result,differences=ndiff) 221 | plot(result) 222 | acf(result) 223 | Box.test(result, lag=10, fitdf=0, type="Lj") 224 | # 7. Use auto.arima() on the time series data obtained after Step #6. What are the values of p, d, and q in ARIMA (p, d, q) model that the model has chosen (use summary() on the model) 225 | result1<-auto.arima(result,approximation=FALSE,trace=FALSE) 226 | # to remove the Warning message: 227 | #In auto.arima(result) : 228 | # Unable to fit final model using maximum likelihood. AIC value approximated 229 | # used -- approximation=FALSE,trace=FALSE) 230 | # ref http://stats.stackexchange.com/questions/89316/warning-message-in-auto-arima 231 | summary(result) 232 | # 8. Generate your own ARIMA(4, 0, 0), ARIMA (3, 0,0) and ARIMA (2, 0, 0) models using the following command: 233 | #Arima(ts.data, order=c(4,0,0)) 234 | Arima(result, order=c(4,0,0)) 235 | Arima(result, order=c(3,0,0)) 236 | Arima(result, order=c(2,0,0)) 237 | summary(result1) 238 | acf(residuals(result)) 239 | acf(residuals(result1)) 240 | Box.test(residuals(result), lag=10, fitdf=0, type="Lj") 241 | Box.test(residuals(result1), lag=10, fitdf=0, type="Lj") 242 | plot(forecast(result)) 243 | plot(forecast(result1)) 244 | cat("\014") 245 | hhelp() 246 | require(fpp) 247 | # load fpp package 248 | library(fpp) 249 | data(dj) 250 | plot(dj) 251 | acf(dj) 252 | Box.test(dj, lag=10, fitdf=0, type="Lj") 253 | temp<-dj 254 | nd<- ndiffs(temp) 255 | if(nd>0){ 256 | temp <- diff(temp,differences=nd) 257 | } 258 | plot(temp) 259 | acf(temp) 260 | Box.test(temp, lag=10, fitdf=0, type="Lj") 261 | require("fma") 262 | require("fpp") 263 | library(package ="fpp") 264 | library(package = "fma") 265 | #1 usnetelec 266 | plot(usnetelec) 267 | #acf(usnetelec) 268 | Box.test(usnetelec, lag=10, fitdf=0, type="Lj") 269 | temp1<-usnetelec 270 | #lambda<-BoxCox.lambda(temp1) 271 | #temp1<-BoxCox(temp1,lambda) 272 | nd<-ndiffs(temp1) 273 | temp1 <- diff(temp1,differences=nd) 274 | plot(temp1) 275 | acf(temp1) 276 | Box.test(temp1, lag=10, fitdf=0, type="Lj") 277 | ################################################### 278 | plot(log(usgdp)) 279 | acf(usnetelec) 280 | Box.test(usnetelec, lag=10, fitdf=0, type="Lj") 281 | temp1<-usnetelec 282 | #lambda<-BoxCox.lambda(temp1) 283 | #temp1<-BoxCox(temp1,lambda) 284 | nd<-ndiffs(temp1) 285 | temp1 <- diff(temp1,differences=nd) 286 | plot(temp1) 287 | acf(temp1) 288 | Box.test(temp1, lag=10, fitdf=0, type="Lj") 289 | require("fma") 290 | require("fpp") 291 | library(package ="fpp") 292 | library(package = "fma") 293 | #1 usnetelec 294 | plot(usnetelec) 295 | acf(usnetelec) 296 | Box.test(usnetelec, lag=10, fitdf=0, type="Lj") 297 | #lambda<-BoxCox.lambda(usnetelec) 298 | #temp<-BoxCox(usnetelec,lambda) 299 | nd<-ndiffs(usnetelec) 300 | temp <- diff(usnetelec,differences=nd) 301 | plot(temp) 302 | acf(temp) 303 | Box.test(temp, lag=10, fitdf=0, type="Lj") 304 | plot(usnetelec) 305 | acf(usnetelec) 306 | Box.test(usnetelec, lag=10, fitdf=0, type="Lj") 307 | lambda<-BoxCox.lambda(usnetelec) 308 | temp<-BoxCox(usnetelec,lambda) 309 | nd<-ndiffs(usnetelec) 310 | temp <- diff(usnetelec,differences=nd) 311 | plot(temp) 312 | acf(temp) 313 | Box.test(temp, lag=10, fitdf=0, type="Lj") 314 | library(igraph) 315 | library(lsa) 316 | graph <- read.graph(file="data/fb_caltech_small_edgelist.txt", format = c("edgelist")) 317 | getcwd() 318 | getwd() 319 | setwd("/home/sud/Desktop/BI/06.Topic-7.Project-6.MarketSegmentation.AttributedGraphCommunityDetection") 320 | graph <- read.graph(file="data/fb_caltech_small_edgelist.txt", format = c("edgelist")) 321 | attrData <- read.csv("data/fb_caltech_small_attrlist.csv") 322 | member <- 1:324 323 | alpha <- 0.5 324 | A = get.adjacency(graph, sparse = FALSE) 325 | S = cosine(A) 326 | print(S) 327 | D = 1-S 328 | # distance object 329 | d = as.dist(D) 330 | find_similarity <- function(ind) 331 | print(d) 332 | graph <- read.graph(file="data/fb_caltech_small_edgelist.txt", format = c("edgelist")) 333 | attrData <- read.csv("data/fb_caltech_small_attrlist.csv") 334 | member <- 1:324 335 | alpha <- 0.5 336 | A = get.adjacency(graph, sparse = FALSE) 337 | S = cosine(A) 338 | print(S) 339 | D = 1-S 340 | # distance object 341 | d = as.dist(D) 342 | print(d) 343 | #install.packages("lsa") 344 | #install.packages("igraph") 345 | #install.packages("plyr") 346 | library(igraph) 347 | library(lsa) 348 | library(plyr) 349 | graph <- read.graph(file="data/fb_caltech_small_edgelist.txt", format = c("edgelist")) 350 | attrData <- read.csv("data/fb_caltech_small_attrlist.csv") 351 | member <- 1:vcount(graph) 352 | alpha <- 1.0 353 | find_similarity <- function(ind,mem,val) 354 | { 355 | temp <- which(mem==val) 356 | len <- length(temp) 357 | ans <- 0.0 358 | for(n in 1:len) 359 | { 360 | ans <- ans + sim[ind,temp[n]] 361 | } 362 | return (ans/(len*len)) 363 | } 364 | sim <- matrix(nrow = vcount(graph),ncol = vcount(graph)) 365 | for (i in 1:vcount(graph)) 366 | { 367 | for (j in i:vcount(graph)) 368 | { 369 | sim[i, j] = cosine(as.numeric(attrData[i,]), as.numeric(attrData[j,])) 370 | sim[j, i]= sim[i, j] 371 | } 372 | } 373 | print(sim) 374 | for(ctr in 1:15) 375 | { 376 | old_member <- member 377 | print(ctr) 378 | for(i in 1:324) 379 | { 380 | QNewman_initial <- modularity(graph, membership = member) 381 | maxval <- -2 382 | index <- i 383 | mem_ini <- member[i] 384 | for(j in 1:324) 385 | { 386 | member[i] = member[j] 387 | QNewman_new <- modularity(graph, membership = member) 388 | QNewman_delta <- QNewman_new - QNewman_initial 389 | QAttr <- find_similarity(i,member,member[j]) / length(unique(member)) 390 | gain <- (alpha*QNewman_delta) + ((1-alpha)*QAttr) 391 | if(gain>maxval) 392 | { 393 | index <- j 394 | maxval <- gain 395 | } 396 | } 397 | member[i] = mem_ini 398 | if(maxval>0) 399 | { 400 | member[i] = member[index] 401 | } 402 | } 403 | print(identical(old_member,member)) 404 | print(member) 405 | if(identical(old_member,member)) 406 | break 407 | } 408 | col<-member 409 | colors <- rainbow(max(col)) 410 | plot(graph,vertex.color=colors[col], layout=layout.fruchterman.reingold, vertex.label= NA) 411 | com=unique(member) 412 | for(i in 1:length(com)){ 413 | t=which(member == com[i]) 414 | t=t-1 415 | write(t, file = "communities.txt", sep = ",", append = TRUE, ncolumns = vcount(graph)) 416 | } 417 | library(igraph) 418 | #install.packages("akmeans") 419 | library("akmeans") 420 | ## Influence Propagation 421 | # Arguments: 422 | # Graph = Structural Graph, Igraph Instance 423 | # Comm = Communiity/Clusters 424 | # Beta = Influence Propagation Rate 425 | influence_propagation <- function(graph, comm, beta=0.75){ 426 | output = c() 427 | v = vcount(graph) 428 | nodes <- data.frame(1:v) 429 | colnames(nodes) <- c("Id") 430 | nodes$comm = comm 431 | nodes$color = "green" 432 | influencers = c() 433 | for(k in unique(comm)){ 434 | influencer = sample(nodes[which(nodes$comm %in% k), c("Id")],1) 435 | influencers = append(influencers, influencer) 436 | } 437 | nodes[nodes$Id %in% influencers, c("color")] <- "red" 438 | new_influencers = influencers 439 | total_influencers = length(influencers) 440 | timestep = 1 441 | old_influencers = 0 442 | ratio = total_influencers/v 443 | cat("Time Step = " , timestep,", Total Influencers = ", total_influencers, ", Network Coverage = " , ratio ,"\n") 444 | output = data.frame(timestep, total_influencers) 445 | timestep = timestep + 1 446 | alpha = 0.7 447 | #while(total_influencers - old_influencers > 0 && ratio < 1){ 448 | for(i in 1:15){ 449 | old_influencers = total_influencers 450 | influencers = new_influencers 451 | for(i in influencers){ 452 | nbs = neighbors(graph, i) 453 | ## Selection if the propagation is inside community or outside 454 | flag = "inside" 455 | if(sample(1:100, 1) <= alpha*100){ 456 | candidates = nodes[nodes$comm == nodes[i, c("comm")] & nodes$color == "green", c("Id")] 457 | candidates = intersect(candidates, nbs) 458 | ids <- c() 459 | for(j in candidates){ 460 | if(j %in% nbs && sample(1:100, 1) <= beta * 100){ # Stochastically determining if node will get infected, using beta transmission probability 461 | ids <- append(ids, j) 462 | } 463 | } 464 | }else{ 465 | candidates = nodes[nodes$comm != nodes[i, c("comm")] & nodes$color == "green", c("Id")] 466 | candidates = intersect(candidates, nbs) 467 | ids <- c() 468 | for(j in candidates){ 469 | if(sample(1:100, 1) <= beta * 100){ # Stochastically determining if node will get infected, using beta transmission probability 470 | ids <- append(ids, j) 471 | } 472 | } 473 | } 474 | nodes[which(nodes$Id %in% ids), c("color")] <- "red" 475 | total_influencers = nrow(nodes[nodes$color == "red",]) 476 | new_influencers = append(new_influencers, unique(ids)) 477 | } 478 | ratio = nrow(nodes[nodes$color == "red",])/v 479 | cat("Time Step = " ,timestep,", Total Influencers = ", total_influencers, ", Network Coverage = ", ratio ,"\n") 480 | output = rbind(output, data.frame(timestep, total_influencers)) 481 | timestep = timestep + 1 482 | } 483 | output 484 | } 485 | ## Influence Propagation Example 486 | # ===== Reading the graph ====== # 487 | g <- read.graph(file="data/fb_caltech_small_edgelist.txt", format = c("edgelist")) 488 | # ===== Read communities file ==== # 489 | comm = integer(vcount(g)) 490 | conn <- file("communities.txt", open="r") 491 | lines <-readLines(conn) 492 | for (i in 1:length(lines)){ 493 | verts = unlist(strsplit(lines[i], ",")) 494 | verts = as.integer(verts) + 1 # R indexes start at 1 495 | comm[verts] = i 496 | } 497 | close(conn) 498 | # ===== Clusters From Adaptive K-means ===== # 499 | attrData <- read.csv("data/fb_caltech_small_attrlist.csv") 500 | akm = akmeans(attrData, d.metric=2, ths3=.4, mode=3, 501 | min.k=length(unique(comm)), max.k=length(unique(comm))) ## cosine distance based 502 | # ===== Influence Propagation Using adaptive k-means clusters ===== # 503 | op1 = influence_propagation(graph=g, comm = akm$cluster) 504 | # ===== Influence Propagation Using SAC-1 communities ===== # 505 | op2 = influence_propagation(graph=g, comm = comm) 506 | print("here") 507 | ## Plot for the timestep comparison 508 | tsteps = as.vector(op1$timestep) 509 | tsteps = append(tsteps, as.vector(op2$timestep)) 510 | plot(op1, type="b", col=2, pch=20, ylim = c(0, vcount(g)), xlim = c(1, max(tsteps)), main = "Influence Propagation") 511 | points(op2, type="b", col=3, pch=20) 512 | legend("bottomright", c("Adaptive-Kmeans","SAC-1"), col=2:3, pch=c(20,20)) 513 | -------------------------------------------------------------------------------- /data/fb_caltech_small_attrlist.csv: 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| 44 119 1547 | 44 120 1548 | 44 121 1549 | 44 125 1550 | 44 126 1551 | 44 127 1552 | 44 135 1553 | 44 136 1554 | 44 144 1555 | 44 147 1556 | 44 151 1557 | 44 153 1558 | 44 154 1559 | 44 155 1560 | 44 156 1561 | 44 157 1562 | 44 158 1563 | 44 159 1564 | 44 164 1565 | 44 167 1566 | 44 168 1567 | 44 169 1568 | 44 171 1569 | 44 177 1570 | 44 180 1571 | 44 186 1572 | 44 187 1573 | 44 189 1574 | 44 190 1575 | 44 191 1576 | 44 192 1577 | 44 193 1578 | 44 194 1579 | 44 196 1580 | 44 198 1581 | 44 199 1582 | 44 200 1583 | 44 202 1584 | 44 203 1585 | 44 204 1586 | 44 205 1587 | 44 206 1588 | 44 209 1589 | 44 210 1590 | 44 212 1591 | 44 213 1592 | 44 214 1593 | 44 215 1594 | 44 218 1595 | 44 221 1596 | 44 223 1597 | 44 229 1598 | 44 230 1599 | 44 232 1600 | 44 235 1601 | 44 237 1602 | 44 239 1603 | 44 240 1604 | 44 242 1605 | 44 250 1606 | 44 255 1607 | 44 258 1608 | 44 260 1609 | 44 261 1610 | 44 263 1611 | 44 268 1612 | 44 270 1613 | 44 274 1614 | 44 277 1615 | 44 278 1616 | 44 279 1617 | 44 280 1618 | 44 282 1619 | 44 284 1620 | 44 289 1621 | 44 290 1622 | 44 291 1623 | 44 295 1624 | 44 297 1625 | 44 298 1626 | 44 299 1627 | 44 300 1628 | 44 308 1629 | 44 310 1630 | 44 313 1631 | 44 315 1632 | 44 323 1633 | 45 52 1634 | 45 53 1635 | 45 57 1636 | 45 61 1637 | 45 64 1638 | 45 68 1639 | 45 69 1640 | 45 73 1641 | 45 87 1642 | 45 112 1643 | 45 120 1644 | 45 122 1645 | 45 126 1646 | 45 136 1647 | 45 144 1648 | 45 147 1649 | 45 151 1650 | 45 153 1651 | 45 167 1652 | 45 169 1653 | 45 180 1654 | 45 186 1655 | 45 190 1656 | 45 194 1657 | 45 202 1658 | 45 203 1659 | 45 204 1660 | 45 205 1661 | 45 209 1662 | 45 210 1663 | 45 213 1664 | 45 214 1665 | 45 230 1666 | 45 232 1667 | 45 233 1668 | 45 237 1669 | 45 239 1670 | 45 240 1671 | 45 242 1672 | 45 250 1673 | 45 260 1674 | 45 268 1675 | 45 270 1676 | 45 272 1677 | 45 274 1678 | 45 280 1679 | 45 284 1680 | 45 289 1681 | 45 292 1682 | 45 310 1683 | 45 313 1684 | 45 315 1685 | 45 316 1686 | 46 188 1687 | 46 291 1688 | 47 186 1689 | 48 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1762 | 52 209 1763 | 52 210 1764 | 52 214 1765 | 52 218 1766 | 52 232 1767 | 52 237 1768 | 52 261 1769 | 52 268 1770 | 52 280 1771 | 52 292 1772 | 52 316 1773 | 53 57 1774 | 53 61 1775 | 53 64 1776 | 53 68 1777 | 53 69 1778 | 53 73 1779 | 53 87 1780 | 53 96 1781 | 53 104 1782 | 53 110 1783 | 53 113 1784 | 53 116 1785 | 53 120 1786 | 53 125 1787 | 53 126 1788 | 53 136 1789 | 53 144 1790 | 53 147 1791 | 53 151 1792 | 53 153 1793 | 53 154 1794 | 53 167 1795 | 53 168 1796 | 53 169 1797 | 53 180 1798 | 53 186 1799 | 53 190 1800 | 53 193 1801 | 53 194 1802 | 53 199 1803 | 53 200 1804 | 53 202 1805 | 53 203 1806 | 53 204 1807 | 53 205 1808 | 53 209 1809 | 53 210 1810 | 53 213 1811 | 53 214 1812 | 53 221 1813 | 53 223 1814 | 53 229 1815 | 53 230 1816 | 53 232 1817 | 53 233 1818 | 53 237 1819 | 53 239 1820 | 53 242 1821 | 53 250 1822 | 53 260 1823 | 53 268 1824 | 53 270 1825 | 53 274 1826 | 53 280 1827 | 53 284 1828 | 53 289 1829 | 53 292 1830 | 53 310 1831 | 53 313 1832 | 53 315 1833 | 53 316 1834 | 53 321 1835 | 53 323 1836 | 54 261 1837 | 55 58 1838 | 55 67 1839 | 55 70 1840 | 55 77 1841 | 55 78 1842 | 55 82 1843 | 55 84 1844 | 55 89 1845 | 55 97 1846 | 55 99 1847 | 55 111 1848 | 55 115 1849 | 55 116 1850 | 55 130 1851 | 55 137 1852 | 55 140 1853 | 55 146 1854 | 55 149 1855 | 55 152 1856 | 55 170 1857 | 55 172 1858 | 55 191 1859 | 55 197 1860 | 55 201 1861 | 55 207 1862 | 55 209 1863 | 55 211 1864 | 55 215 1865 | 55 217 1866 | 55 219 1867 | 55 220 1868 | 55 225 1869 | 55 228 1870 | 55 233 1871 | 55 238 1872 | 55 239 1873 | 55 240 1874 | 55 253 1875 | 55 255 1876 | 55 264 1877 | 55 267 1878 | 55 269 1879 | 55 272 1880 | 55 273 1881 | 55 283 1882 | 55 285 1883 | 55 291 1884 | 55 294 1885 | 55 305 1886 | 55 306 1887 | 55 316 1888 | 55 317 1889 | 55 318 1890 | 55 321 1891 | 56 65 1892 | 56 74 1893 | 56 86 1894 | 56 90 1895 | 56 99 1896 | 56 113 1897 | 56 124 1898 | 56 145 1899 | 56 162 1900 | 56 195 1901 | 56 214 1902 | 56 215 1903 | 56 231 1904 | 56 240 1905 | 56 245 1906 | 56 263 1907 | 56 286 1908 | 56 288 1909 | 56 290 1910 | 56 299 1911 | 56 309 1912 | 56 314 1913 | 56 320 1914 | 56 322 1915 | 57 61 1916 | 57 64 1917 | 57 68 1918 | 57 69 1919 | 57 72 1920 | 57 73 1921 | 57 76 1922 | 57 87 1923 | 57 95 1924 | 57 96 1925 | 57 98 1926 | 57 100 1927 | 57 104 1928 | 57 106 1929 | 57 107 1930 | 57 110 1931 | 57 112 1932 | 57 113 1933 | 57 120 1934 | 57 121 1935 | 57 122 1936 | 57 125 1937 | 57 126 1938 | 57 127 1939 | 57 136 1940 | 57 144 1941 | 57 147 1942 | 57 153 1943 | 57 154 1944 | 57 157 1945 | 57 158 1946 | 57 159 1947 | 57 167 1948 | 57 168 1949 | 57 169 1950 | 57 177 1951 | 57 180 1952 | 57 186 1953 | 57 189 1954 | 57 190 1955 | 57 193 1956 | 57 194 1957 | 57 196 1958 | 57 199 1959 | 57 200 1960 | 57 202 1961 | 57 203 1962 | 57 204 1963 | 57 205 1964 | 57 209 1965 | 57 210 1966 | 57 214 1967 | 57 215 1968 | 57 218 1969 | 57 221 1970 | 57 223 1971 | 57 229 1972 | 57 230 1973 | 57 232 1974 | 57 237 1975 | 57 242 1976 | 57 244 1977 | 57 250 1978 | 57 255 1979 | 57 259 1980 | 57 261 1981 | 57 268 1982 | 57 273 1983 | 57 279 1984 | 57 280 1985 | 57 282 1986 | 57 284 1987 | 57 291 1988 | 57 292 1989 | 57 297 1990 | 57 308 1991 | 57 310 1992 | 57 313 1993 | 57 316 1994 | 57 323 1995 | 58 67 1996 | 58 77 1997 | 58 84 1998 | 58 89 1999 | 58 97 2000 | 58 104 2001 | 58 115 2002 | 58 116 2003 | 58 129 2004 | 58 137 2005 | 58 140 2006 | 58 149 2007 | 58 151 2008 | 58 193 2009 | 58 197 2010 | 58 207 2011 | 58 213 2012 | 58 219 2013 | 58 225 2014 | 58 238 2015 | 58 240 2016 | 58 264 2017 | 58 285 2018 | 58 291 2019 | 58 294 2020 | 58 300 2021 | 58 305 2022 | 58 316 2023 | 58 321 2024 | 59 70 2025 | 59 75 2026 | 59 78 2027 | 59 90 2028 | 59 116 2029 | 59 129 2030 | 59 145 2031 | 59 192 2032 | 59 212 2033 | 59 231 2034 | 59 263 2035 | 59 273 2036 | 59 280 2037 | 59 290 2038 | 59 293 2039 | 59 303 2040 | 59 304 2041 | 59 314 2042 | 59 322 2043 | 60 63 2044 | 60 68 2045 | 60 190 2046 | 60 197 2047 | 60 204 2048 | 60 278 2049 | 60 289 2050 | 60 296 2051 | 60 305 2052 | 61 63 2053 | 61 64 2054 | 61 69 2055 | 61 73 2056 | 61 126 2057 | 61 167 2058 | 61 169 2059 | 61 180 2060 | 61 190 2061 | 61 193 2062 | 61 194 2063 | 61 199 2064 | 61 204 2065 | 61 205 2066 | 61 209 2067 | 61 213 2068 | 61 216 2069 | 61 255 2070 | 61 268 2071 | 61 274 2072 | 61 289 2073 | 61 308 2074 | 62 65 2075 | 62 75 2076 | 62 99 2077 | 62 103 2078 | 62 115 2079 | 62 132 2080 | 62 139 2081 | 62 144 2082 | 62 169 2083 | 62 182 2084 | 62 188 2085 | 62 195 2086 | 62 212 2087 | 62 218 2088 | 62 224 2089 | 62 226 2090 | 62 246 2091 | 62 249 2092 | 62 258 2093 | 62 261 2094 | 62 280 2095 | 62 286 2096 | 62 290 2097 | 62 314 2098 | 62 322 2099 | 63 64 2100 | 63 82 2101 | 63 85 2102 | 63 115 2103 | 63 116 2104 | 63 130 2105 | 63 132 2106 | 63 137 2107 | 63 149 2108 | 63 172 2109 | 63 191 2110 | 63 197 2111 | 63 201 2112 | 63 204 2113 | 63 207 2114 | 63 213 2115 | 63 216 2116 | 63 219 2117 | 63 225 2118 | 63 233 2119 | 63 234 2120 | 63 240 2121 | 63 264 2122 | 63 278 2123 | 63 289 2124 | 63 291 2125 | 63 294 2126 | 63 305 2127 | 63 317 2128 | 64 68 2129 | 64 69 2130 | 64 73 2131 | 64 87 2132 | 64 99 2133 | 64 111 2134 | 64 116 2135 | 64 120 2136 | 64 122 2137 | 64 126 2138 | 64 132 2139 | 64 144 2140 | 64 147 2141 | 64 151 2142 | 64 153 2143 | 64 167 2144 | 64 168 2145 | 64 169 2146 | 64 180 2147 | 64 186 2148 | 64 194 2149 | 64 199 2150 | 64 202 2151 | 64 203 2152 | 64 204 2153 | 64 205 2154 | 64 209 2155 | 64 210 2156 | 64 213 2157 | 64 214 2158 | 64 232 2159 | 64 233 2160 | 64 237 2161 | 64 239 2162 | 64 250 2163 | 64 255 2164 | 64 268 2165 | 64 272 2166 | 64 274 2167 | 64 280 2168 | 64 284 2169 | 64 289 2170 | 64 308 2171 | 64 313 2172 | 64 315 2173 | 64 316 2174 | 64 322 2175 | 65 71 2176 | 65 72 2177 | 65 75 2178 | 65 88 2179 | 65 90 2180 | 65 99 2181 | 65 101 2182 | 65 104 2183 | 65 108 2184 | 65 115 2185 | 65 124 2186 | 65 132 2187 | 65 134 2188 | 65 143 2189 | 65 175 2190 | 65 182 2191 | 65 188 2192 | 65 199 2193 | 65 212 2194 | 65 224 2195 | 65 226 2196 | 65 231 2197 | 65 232 2198 | 65 245 2199 | 65 246 2200 | 65 249 2201 | 65 258 2202 | 65 260 2203 | 65 271 2204 | 65 280 2205 | 65 286 2206 | 65 287 2207 | 65 290 2208 | 65 299 2209 | 65 300 2210 | 65 302 2211 | 65 303 2212 | 65 304 2213 | 65 307 2214 | 65 311 2215 | 65 314 2216 | 65 320 2217 | 66 75 2218 | 66 80 2219 | 66 88 2220 | 66 103 2221 | 66 107 2222 | 66 108 2223 | 66 132 2224 | 66 182 2225 | 66 212 2226 | 66 226 2227 | 66 249 2228 | 66 271 2229 | 66 278 2230 | 66 286 2231 | 66 299 2232 | 66 312 2233 | 66 314 2234 | 67 70 2235 | 67 74 2236 | 67 77 2237 | 67 78 2238 | 67 82 2239 | 67 84 2240 | 67 89 2241 | 67 90 2242 | 67 93 2243 | 67 97 2244 | 67 111 2245 | 67 112 2246 | 67 114 2247 | 67 115 2248 | 67 116 2249 | 67 119 2250 | 67 126 2251 | 67 129 2252 | 67 137 2253 | 67 140 2254 | 67 144 2255 | 67 146 2256 | 67 149 2257 | 67 152 2258 | 67 162 2259 | 67 167 2260 | 67 170 2261 | 67 186 2262 | 67 188 2263 | 67 191 2264 | 67 192 2265 | 67 196 2266 | 67 197 2267 | 67 201 2268 | 67 202 2269 | 67 207 2270 | 67 211 2271 | 67 212 2272 | 67 215 2273 | 67 217 2274 | 67 218 2275 | 67 219 2276 | 67 220 2277 | 67 225 2278 | 67 228 2279 | 67 230 2280 | 67 232 2281 | 67 233 2282 | 67 234 2283 | 67 238 2284 | 67 239 2285 | 67 240 2286 | 67 255 2287 | 67 257 2288 | 67 260 2289 | 67 263 2290 | 67 264 2291 | 67 267 2292 | 67 269 2293 | 67 272 2294 | 67 273 2295 | 67 277 2296 | 67 283 2297 | 67 290 2298 | 67 291 2299 | 67 294 2300 | 67 305 2301 | 67 308 2302 | 67 316 2303 | 67 318 2304 | 68 73 2305 | 68 92 2306 | 68 99 2307 | 68 112 2308 | 68 122 2309 | 68 147 2310 | 68 164 2311 | 68 167 2312 | 68 169 2313 | 68 186 2314 | 68 200 2315 | 68 204 2316 | 68 213 2317 | 68 260 2318 | 68 268 2319 | 68 284 2320 | 68 289 2321 | 68 310 2322 | 69 72 2323 | 69 73 2324 | 69 76 2325 | 69 96 2326 | 69 100 2327 | 69 104 2328 | 69 106 2329 | 69 113 2330 | 69 119 2331 | 69 120 2332 | 69 121 2333 | 69 125 2334 | 69 126 2335 | 69 127 2336 | 69 136 2337 | 69 147 2338 | 69 154 2339 | 69 155 2340 | 69 157 2341 | 69 159 2342 | 69 162 2343 | 69 168 2344 | 69 169 2345 | 69 180 2346 | 69 189 2347 | 69 190 2348 | 69 194 2349 | 69 199 2350 | 69 202 2351 | 69 203 2352 | 69 204 2353 | 69 205 2354 | 69 209 2355 | 69 210 2356 | 69 223 2357 | 69 229 2358 | 69 232 2359 | 69 235 2360 | 69 237 2361 | 69 242 2362 | 69 244 2363 | 69 255 2364 | 69 257 2365 | 69 260 2366 | 69 268 2367 | 69 280 2368 | 69 282 2369 | 69 308 2370 | 69 310 2371 | 69 313 2372 | 69 323 2373 | 70 79 2374 | 70 84 2375 | 70 89 2376 | 70 93 2377 | 70 97 2378 | 70 111 2379 | 70 114 2380 | 70 116 2381 | 70 129 2382 | 70 134 2383 | 70 140 2384 | 70 146 2385 | 70 161 2386 | 70 169 2387 | 70 170 2388 | 70 192 2389 | 70 197 2390 | 70 202 2391 | 70 209 2392 | 70 215 2393 | 70 219 2394 | 70 220 2395 | 70 225 2396 | 70 228 2397 | 70 233 2398 | 70 238 2399 | 70 240 2400 | 70 255 2401 | 70 260 2402 | 70 263 2403 | 70 264 2404 | 70 265 2405 | 70 267 2406 | 70 272 2407 | 70 273 2408 | 70 277 2409 | 70 280 2410 | 70 305 2411 | 70 316 2412 | 70 317 2413 | 70 318 2414 | 70 319 2415 | 71 99 2416 | 71 143 2417 | 71 171 2418 | 71 175 2419 | 71 245 2420 | 71 299 2421 | 71 302 2422 | 72 126 2423 | 72 135 2424 | 72 144 2425 | 72 154 2426 | 72 155 2427 | 72 157 2428 | 72 159 2429 | 72 176 2430 | 72 194 2431 | 72 202 2432 | 72 212 2433 | 72 223 2434 | 72 229 2435 | 72 235 2436 | 72 237 2437 | 72 244 2438 | 72 261 2439 | 72 280 2440 | 72 292 2441 | 72 303 2442 | 72 323 2443 | 73 87 2444 | 73 96 2445 | 73 119 2446 | 73 120 2447 | 73 126 2448 | 73 136 2449 | 73 144 2450 | 73 147 2451 | 73 151 2452 | 73 153 2453 | 73 154 2454 | 73 167 2455 | 73 168 2456 | 73 169 2457 | 73 180 2458 | 73 186 2459 | 73 190 2460 | 73 193 2461 | 73 194 2462 | 73 197 2463 | 73 199 2464 | 73 200 2465 | 73 202 2466 | 73 204 2467 | 73 205 2468 | 73 209 2469 | 73 213 2470 | 73 214 2471 | 73 232 2472 | 73 233 2473 | 73 237 2474 | 73 250 2475 | 73 260 2476 | 73 261 2477 | 73 264 2478 | 73 268 2479 | 73 272 2480 | 73 274 2481 | 73 278 2482 | 73 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| 75 295 2555 | 75 299 2556 | 75 303 2557 | 75 304 2558 | 75 312 2559 | 75 314 2560 | 75 320 2561 | 75 322 2562 | 76 100 2563 | 76 113 2564 | 76 125 2565 | 76 126 2566 | 76 136 2567 | 76 144 2568 | 76 147 2569 | 76 154 2570 | 76 157 2571 | 76 158 2572 | 76 159 2573 | 76 168 2574 | 76 169 2575 | 76 192 2576 | 76 194 2577 | 76 196 2578 | 76 199 2579 | 76 237 2580 | 76 245 2581 | 76 255 2582 | 76 261 2583 | 76 263 2584 | 76 282 2585 | 76 291 2586 | 76 313 2587 | 77 84 2588 | 77 89 2589 | 77 111 2590 | 77 115 2591 | 77 137 2592 | 77 211 2593 | 77 238 2594 | 77 240 2595 | 77 255 2596 | 77 257 2597 | 77 262 2598 | 77 280 2599 | 77 317 2600 | 77 321 2601 | 78 82 2602 | 78 89 2603 | 78 90 2604 | 78 99 2605 | 78 112 2606 | 78 115 2607 | 78 116 2608 | 78 140 2609 | 78 152 2610 | 78 164 2611 | 78 167 2612 | 78 175 2613 | 78 188 2614 | 78 197 2615 | 78 201 2616 | 78 207 2617 | 78 212 2618 | 78 215 2619 | 78 217 2620 | 78 218 2621 | 78 219 2622 | 78 226 2623 | 78 229 2624 | 78 230 2625 | 78 233 2626 | 78 238 2627 | 78 245 2628 | 78 246 2629 | 78 249 2630 | 78 255 2631 | 78 263 2632 | 78 264 2633 | 78 280 2634 | 78 283 2635 | 78 290 2636 | 78 291 2637 | 78 294 2638 | 78 299 2639 | 78 305 2640 | 78 314 2641 | 78 316 2642 | 78 318 2643 | 78 321 2644 | 78 322 2645 | 79 116 2646 | 79 192 2647 | 79 205 2648 | 79 215 2649 | 79 220 2650 | 79 225 2651 | 79 228 2652 | 79 272 2653 | 79 273 2654 | 79 291 2655 | 80 92 2656 | 80 99 2657 | 80 103 2658 | 80 108 2659 | 80 132 2660 | 80 139 2661 | 80 143 2662 | 80 156 2663 | 80 163 2664 | 80 164 2665 | 80 171 2666 | 80 172 2667 | 80 216 2668 | 80 226 2669 | 80 233 2670 | 80 245 2671 | 80 260 2672 | 80 271 2673 | 80 278 2674 | 80 280 2675 | 80 286 2676 | 80 290 2677 | 80 299 2678 | 80 302 2679 | 80 312 2680 | 80 322 2681 | 81 86 2682 | 82 85 2683 | 82 87 2684 | 82 93 2685 | 82 99 2686 | 82 104 2687 | 82 116 2688 | 82 129 2689 | 82 149 2690 | 82 151 2691 | 82 167 2692 | 82 191 2693 | 82 197 2694 | 82 205 2695 | 82 209 2696 | 82 214 2697 | 82 217 2698 | 82 220 2699 | 82 233 2700 | 82 239 2701 | 82 240 2702 | 82 255 2703 | 82 260 2704 | 82 264 2705 | 82 272 2706 | 82 305 2707 | 82 318 2708 | 82 322 2709 | 84 89 2710 | 84 111 2711 | 84 115 2712 | 84 116 2713 | 84 127 2714 | 84 137 2715 | 84 146 2716 | 84 149 2717 | 84 152 2718 | 84 191 2719 | 84 197 2720 | 84 205 2721 | 84 207 2722 | 84 211 2723 | 84 217 2724 | 84 219 2725 | 84 220 2726 | 84 225 2727 | 84 228 2728 | 84 233 2729 | 84 238 2730 | 84 239 2731 | 84 240 2732 | 84 260 2733 | 84 264 2734 | 84 267 2735 | 84 272 2736 | 84 273 2737 | 84 291 2738 | 84 294 2739 | 84 305 2740 | 84 317 2741 | 84 318 2742 | 84 321 2743 | 85 97 2744 | 85 99 2745 | 85 102 2746 | 85 116 2747 | 85 149 2748 | 85 172 2749 | 85 197 2750 | 85 204 2751 | 85 207 2752 | 85 217 2753 | 85 239 2754 | 85 240 2755 | 85 262 2756 | 85 264 2757 | 85 267 2758 | 85 272 2759 | 85 285 2760 | 85 305 2761 | 85 318 2762 | 85 321 2763 | 87 99 2764 | 87 116 2765 | 87 120 2766 | 87 127 2767 | 87 144 2768 | 87 167 2769 | 87 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| 89 233 2842 | 89 238 2843 | 89 239 2844 | 89 240 2845 | 89 264 2846 | 89 267 2847 | 89 272 2848 | 89 273 2849 | 89 283 2850 | 89 294 2851 | 89 305 2852 | 89 318 2853 | 89 321 2854 | 90 113 2855 | 90 124 2856 | 90 125 2857 | 90 131 2858 | 90 136 2859 | 90 140 2860 | 90 144 2861 | 90 162 2862 | 90 171 2863 | 90 183 2864 | 90 201 2865 | 90 202 2866 | 90 208 2867 | 90 215 2868 | 90 218 2869 | 90 221 2870 | 90 224 2871 | 90 229 2872 | 90 231 2873 | 90 240 2874 | 90 241 2875 | 90 246 2876 | 90 255 2877 | 90 261 2878 | 90 263 2879 | 90 275 2880 | 90 280 2881 | 90 286 2882 | 90 288 2883 | 90 290 2884 | 90 291 2885 | 90 293 2886 | 90 300 2887 | 90 303 2888 | 90 309 2889 | 90 319 2890 | 90 320 2891 | 90 322 2892 | 91 163 2893 | 92 99 2894 | 92 108 2895 | 92 132 2896 | 92 156 2897 | 92 164 2898 | 92 212 2899 | 92 245 2900 | 92 249 2901 | 92 271 2902 | 92 286 2903 | 92 290 2904 | 92 299 2905 | 92 304 2906 | 92 312 2907 | 92 314 2908 | 93 115 2909 | 93 116 2910 | 93 146 2911 | 93 170 2912 | 93 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2984 | 97 220 2985 | 97 225 2986 | 97 233 2987 | 97 238 2988 | 97 240 2989 | 97 264 2990 | 97 265 2991 | 97 272 2992 | 97 280 2993 | 97 291 2994 | 97 305 2995 | 97 321 2996 | 98 126 2997 | 98 144 2998 | 98 177 2999 | 98 230 3000 | 98 261 3001 | 99 101 3002 | 99 102 3003 | 99 103 3004 | 99 108 3005 | 99 112 3006 | 99 115 3007 | 99 116 3008 | 99 119 3009 | 99 120 3010 | 99 124 3011 | 99 127 3012 | 99 128 3013 | 99 131 3014 | 99 132 3015 | 99 134 3016 | 99 139 3017 | 99 140 3018 | 99 143 3019 | 99 149 3020 | 99 156 3021 | 99 161 3022 | 99 162 3023 | 99 163 3024 | 99 164 3025 | 99 165 3026 | 99 168 3027 | 99 170 3028 | 99 175 3029 | 99 178 3030 | 99 179 3031 | 99 180 3032 | 99 184 3033 | 99 189 3034 | 99 207 3035 | 99 209 3036 | 99 217 3037 | 99 224 3038 | 99 226 3039 | 99 231 3040 | 99 232 3041 | 99 240 3042 | 99 241 3043 | 99 245 3044 | 99 248 3045 | 99 249 3046 | 99 262 3047 | 99 264 3048 | 99 267 3049 | 99 268 3050 | 99 274 3051 | 99 277 3052 | 99 278 3053 | 99 283 3054 | 99 284 3055 | 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3123 | 103 108 3124 | 103 115 3125 | 103 132 3126 | 103 139 3127 | 103 143 3128 | 103 152 3129 | 103 156 3130 | 103 164 3131 | 103 182 3132 | 103 184 3133 | 103 188 3134 | 103 212 3135 | 103 214 3136 | 103 224 3137 | 103 226 3138 | 103 245 3139 | 103 249 3140 | 103 251 3141 | 103 258 3142 | 103 260 3143 | 103 263 3144 | 103 271 3145 | 103 278 3146 | 103 280 3147 | 103 286 3148 | 103 290 3149 | 103 295 3150 | 103 299 3151 | 103 302 3152 | 103 303 3153 | 103 304 3154 | 103 312 3155 | 103 314 3156 | 103 320 3157 | 103 322 3158 | 104 115 3159 | 104 116 3160 | 104 119 3161 | 104 120 3162 | 104 121 3163 | 104 126 3164 | 104 136 3165 | 104 137 3166 | 104 144 3167 | 104 154 3168 | 104 167 3169 | 104 190 3170 | 104 193 3171 | 104 194 3172 | 104 199 3173 | 104 205 3174 | 104 209 3175 | 104 223 3176 | 104 232 3177 | 104 233 3178 | 104 242 3179 | 104 244 3180 | 104 255 3181 | 104 261 3182 | 104 263 3183 | 104 268 3184 | 104 269 3185 | 104 283 3186 | 104 308 3187 | 104 316 3188 | 104 320 3189 | 104 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