├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 THUDM 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Graph Reading Group 2 | 3 | #### 16 July, 2019 4 | 5 | * [Compositional Fairness Constraints for Graph Embeddings](https://arxiv.org/abs/1905.10674), *Avishek Joey Bose, William L. Hamilton.* ICML 2019. 6 | 7 | #### 13 July, 2019 8 | 9 | * [k-hop Graph Neural Networks](https://arxiv.org/abs/1907.06051), *Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis.* Under NeurIPS 2019 review. 10 | 11 | #### 11 July, 2019 12 | 13 | * [Mincut pooling in Graph Neural Networks](https://arxiv.org/abs/1907.00481), *Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi.* 14 | 15 | * [Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology](https://arxiv.org/abs/1907.05008), *Nima Dehmamy, Albert-László Barabási, Rose Yu.* Under NeurIPS 2019 review. 16 | 17 | #### 10 July, 2019 18 | 19 | * [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://arxiv.org/abs/1907.04931), *Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna.* Under NeurIPS 2019 review. 20 | 21 | #### 6 July, 2019 22 | 23 | * [What graph neural networks cannot learn: depth vs width](https://arxiv.org/abs/1907.03199), *Andreas Loukas.* 24 | 25 | #### 5 July, 2019 26 | 27 | * [Graph Representation Learning via Hard and Channel-Wise Attention Networks](https://arxiv.org/abs/1907.04652), *Hongyang Gao, Shuiwang Ji.* KDD 2019. 28 | 29 | #### 4 July, 2019 30 | 31 | * [Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation](https://arxiv.org/abs/1907.02204), *Shuo Zhang, Lei Xie.* Under NeurIPS 2019 review. 32 | 33 | * [Dimensional Reweighting Graph Convolutional Networks](https://arxiv.org/abs/1907.02237), *Xu Zou, Qiuye Jia, Jianwei Zhang, Chang Zhou, Hongxia Yang, Jie Tang.* Under NeurIPS 2019 review. 34 | 35 | #### 2 July, 2019 36 | 37 | * [dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning](https://arxiv.org/abs/1809.02657), *Palash Goyal, Sujit Rokka Chhetri, Arquimedes Canedo.* Under Knowledge Based Systems review. 38 | 39 | #### 1 July, 2019 40 | 41 | * [iPool -- Information-based Pooling in Hierarchical Graph Neural Networks](https://arxiv.org/abs/1907.00832), *Xing Gao, Hongkai Xiong, Pascal Frossard.* Under NeurIPS 2019 review. 42 | 43 | * [Learning Representations of Graph Data -- A Survey](https://arxiv.org/abs/1906.02989), *Mital Kinderkhedia.* 44 | 45 | #### 30 June, 2019 46 | 47 | * [Fisher-Bures Adversary Graph Convolutional Networks](https://arxiv.org/abs/1903.04154), *Ke Sun, Piotr Koniusz, Zhen Wang.* 48 | 49 | #### 28 June, 2019 50 | 51 | * [GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation](https://arxiv.org/abs/1906.12192), *Marc Brockschmidt.* 52 | 53 | * [Certifiable Robustness and Robust Training for Graph Convolutional Networks](https://arxiv.org/abs/1906.12269), *Daniel Zügner, Stephan Günnemann.* KDD 2019 54 | 55 | * [Label Efficient Semi-Supervised Learning via Graph Filtering](https://arxiv.org/abs/1901.09993), *Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan.* 56 | 57 | * [Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records](https://arxiv.org/abs/1906.04716), *Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai.* Under NeurIPS 2019 review. 58 | 59 | #### 27 June, 2019 60 | 61 | * [Fast Training of Sparse Graph Neural Networks on Dense Hardware](https://arxiv.org/abs/1906.11786), *Matej Balog, Bart van Merriënboer, Subhodeep Moitra, Yujia Li, Daniel Tarlow.* Under NeurIPS 2019 review. 62 | 63 | * [Adversarial Representation Learning on Large-Scale Bipartite Graphs](https://arxiv.org/abs/1906.11994), *Chaoyang He, Tian Xie, Yu Rong, Wenbing Huang, Junzhou Huang, Xiang Ren, Cyrus Shahabi.* Under NeurIPS 2019 review. 64 | 65 | * [Inference in Probabilistic Graphical Models by Graph Neural Networks](https://arxiv.org/abs/1803.07710), *KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow.* ICML 2019 Workshop 66 | 67 | #### 26 June, 2019 68 | 69 | * [Provably Powerful Graph Networks](https://arxiv.org/abs/1905.11136), *Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman.* Under NeurIPS 2019 review. 70 | 71 | #### 20 June, 2019 72 | 73 | * [Simplifying Graph Convolutional Networks](https://arxiv.org/abs/1902.07153), *Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger.* ICML 2019 74 | 75 | * [A Graph Auto-Encoder for Attributed Network Embedding](https://arxiv.org/abs/1906.08745), *Keting Cen, Huawei Shen, Jinhua Gao, Qi Cao, Bingbing Xu, Xueqi Cheng.* 76 | 77 | #### 19 June, 2019 78 | 79 | * [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237), *Zhilin Yang∗, Zihang Dai∗, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.* Under NeurIPS 2019 review. 80 | 81 | #### 18 June, 2019 82 | 83 | * [vGraph: A Generative Model for Joint Community Detection and Node Representation Learning](https://arxiv.org/abs/1906.07159), *Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang.* 84 | 85 | * [Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods](https://arxiv.org/abs/1906.07658), *Franca Hoffmann, Bamdad Hosseini, Zhi Ren, Andrew M. Stuart.* 86 | 87 | #### 17 June, 2019 88 | 89 | * [Homogeneous Network Embedding for Massive Graphs via Personalized PageRank](https://arxiv.org/abs/1906.06826), *Renchi Yang, Jieming Shi, Xiaokui Xiao, Sourav S. Bhowmick, Yin Yang.* 90 | 91 | #### 15 June, 2019 92 | 93 | * [Attributed Graph Clustering: A Deep Attentional Embedding Approach](https://arxiv.org/abs/1906.06532), *Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang.* 94 | 95 | #### 14 June, 2019 96 | 97 | * [Disentangling Mixtures of Epidemics on Graphs](https://arxiv.org/abs/1906.06057), *Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis.* Under NeurIPS 2019 review. 98 | 99 | #### 13 June, 2019 100 | 101 | * [Cognitive Knowledge Graph Reasoning for One-shot Relational Learning](https://arxiv.org/pdf/1906.05489.pdf), *Zhengxiao Du, Chang Zhou, Ming Ding, Hongxia Yang, Jie Tang.* Under NeurIPS 2019 review. 102 | 103 | * [Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach](https://arxiv.org/abs/1906.05546), *Da Sun Handason Tam, Wing Cheong Lau, Bin Hu, Qiu Fang Ying, Dah Ming Chiu, Hong Liu.* 104 | 105 | * [Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization](https://arxiv.org/abs/1906.05488), *Pengfei Chen, Weiwen Liu, Chang-Yu Hsieh, Guangyong Chen, Shengyu Zhang.* Under NeurIPS 2019 review. 106 | 107 | * [Position-aware Graph Neural Networks](https://arxiv.org/abs/1906.04817), *Jiaxuan You, Rex Ying, Jure Leskovec.* ICML 2019 108 | 109 | #### 12 June, 2019 110 | 111 | * [Weight Agnostic Neural Networks](https://arxiv.org/abs/1906.04358), *Adam Gaier, David Ha.* Under NeurIPS 2019 review. 112 | 113 | * [Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations](https://arxiv.org/abs/1906.05017), *Xiang Yue, Zhen Wang, Jingong Huang, Srinivasan Parthasarathy, Soheil Moosavinasab, Yungui Huang, Simon M. Lin, Wen Zhang, Ping Zhang, Huan Sun.* Under Bioinformatics journal review. 114 | 115 | * [Multiple instance learning with graph neural networks](https://arxiv.org/abs/1906.04881), *Ming Tu, Jing Huang, Xiaodong He, Bowen Zhou.* ICML 2019 Workshop. 116 | 117 | #### 11 June, 2019 118 | 119 | * [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://arxiv.org/abs/1811.12359), *Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem.* ICML 2019 best paper award 120 | 121 | * [Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records](https://arxiv.org/abs/1906.04716), *Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai.* Under NeurIPS 2019 review. 122 | 123 | #### 10 June, 2019 124 | 125 | * [Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective](https://arxiv.org/abs/1906.04214), *Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin.* IJCAI 2019 126 | 127 | #### 9 June, 2019 128 | 129 | * [Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks](https://arxiv.org/abs/1906.04580), *Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai, Philip S. Yu.* IJCAI 2019 130 | 131 | #### 7 June, 2019 132 | 133 | * [Labeled Graph Generative Adversarial Networks](https://arxiv.org/abs/1906.03220), *Shuangfei Fan, Bert Huang.* Under NeurIPS 2019 review. 134 | 135 | #### 6 June, 2019 136 | 137 | * [Dynamically Fused Graph Network for Multi-hop Reasoning](https://arxiv.org/abs/1905.06933), *Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu.* ACL 19. 138 | 139 | #### 5 June, 2019 140 | 141 | * [Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks](https://arxiv.org/abs/1906.02174), *Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup.* Under NeurIPS 2019 review. 142 | 143 | * [Can Graph Neural Networks Help Logic Reasoning?](https://arxiv.org/abs/1906.02111), *Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song.* Under NeurIPS 2019 review. 144 | 145 | * [GRAM: Scalable Generative Models for Graphs with Graph Attention Mechanism](https://arxiv.org/abs/1906.01861), *Wataru Kawai, Yusuke Mukuta, Tatsuya Harada.* Under NeurIPS 2019 review. 146 | 147 | * [Variational Spectral Graph Convolutional Networks](https://arxiv.org/abs/1906.01852), *Louis Tiao, Pantelis Elinas, Harrison Nguyen, Edwin V. Bonilla.* Under NeurIPS 2019 review. 148 | 149 | * [Binarized Collaborative Filtering with Distilling Graph Convolutional Networks](https://arxiv.org/abs/1906.01829), *Haoyu Wang, Defu Lian, Yong Ge.* 150 | 151 | * [DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification](https://arxiv.org/abs/1906.02319), *Jun Wu, Jingrui He, Jiejun Xu.* KDD 2019 Research Track. 152 | 153 | * [Graph Learning Network: A Structure Learning Algorithm](https://arxiv.org/abs/1905.12665), *Darwin Saire Pilco, Adín Ramírez Rivera.* ICML 2019 Workshop. 154 | 155 | #### 3 June, 2019 156 | 157 | * [Factor Graph Neural Network](https://arxiv.org/abs/1906.00554), *Zhen Zhang, Fan Wu, Wee Sun Lee.* Under NeurIPS 2019 review. 158 | 159 | #### 2 June, 2019 160 | 161 | * [Pre-training of Graph Augmented Transformers for Medication Recommendation](https://arxiv.org/abs/1906.00346), *Junyuan Shang, Tengfei Ma, Cao Xiao, Jimeng Sun.* IJCAI 2019. 162 | 163 | #### 31 May, 2019 164 | 165 | * [Pre-Training Graph Neural Networks for 166 | Generic Structural Feature Extraction](https://arxiv.org/abs/1905.13728), *Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, Yizhou Sun.* Under NeurIPS 2019 review. 167 | 168 | #### 30 May, 2019 169 | 170 | * [Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels](https://arxiv.org/abs/1905.13192), *Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu.* 171 | 172 | * [EdMot: An Edge Enhancement Approach for Motif-aware Community Detection](https://arxiv.org/abs/1906.04560), *Pei-Zhen Li, Ling Huang, Chang-Dong Wang, Jian-Huang Lai.* KDD 2019 173 | 174 | #### 29 May, 2019 175 | 176 | * [Pre-training Graph Neural Networks](https://arxiv.org/abs/1905.12265), *Weihua Hu\*, Bowen Liu\*, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, Jure Leskovec.* Under NeurIPS 2019 review. 177 | 178 | 179 | --------------------------------------------------------------------------------