ICML2021上的图神经网络好文
发布日期：2022-03-31 04:14 点击次数：79
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今年的ICML录取结果出了，图神经网络也是今年的一大热点，这里总结一部分我看到的GNN的文章，如果有错误的或者遗漏的文章请大家一定指出来。整理不易，点个赞呗再走呗，欢迎关注我们的新专栏图神经网络实战，里面会更新一些图神经网络代码解读和最新的进展。如果大家有什么需要分享的，也欢迎投稿本专栏，感激不尽。因为好多文章还挂出来，这里先给个标题，等开会的时候再补上链接哈~GNN理论Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth Keyulu Xu (MIT) · Mozhi Zhang (University of Maryland) · Stefanie Jegelka (Massachusetts Institute of Technology) · Kenji Kawaguchi (MIT) Global optimality of linear GNN optimization w/o JK Information Obfuscation of Graph Neural Networks Peiyuan Liao (Carnegie Mellon University) · Han Zhao (University of Illinois at Urbana-Champaign) · Keyulu Xu (MIT) · Tommi Jaakkola (MIT) · Geoff Gordon (Carnegie Mellon University) · Stefanie Jegelka (Massachusetts Institute of Technology) · Ruslan Salakhutdinov (Carnegie Mellen University) fundamental tradeoffs in privacy and accuracyLet's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework Floris Geerts (University of Antwerp) · Filip Mazowiecki (MPI-SWS) · Guillermo Perez (UAntwerpen) power of GNNs with degree features and WL test?A Unified Lottery Ticket Hypothesis for Graph Neural Networks Tianlong Chen (University of Texas at Austin) · Yongduo Sui (University of Science and Technology of China) · Xuxi Chen (University of Texas at Austin) · Aston Zhang (AWS AI) · Zhangyang Wang (University of Texas at Austin)Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach Federico Lopez (HITS - Heidelberg Institute for Theoretical Studies) · Beatrice Pozzetti (Heidelberg University) · Steve Trettel (Stanford University) · Michael Strube (Heidelberg Institute for Theoretical Studies) · Anna Wienhard (Heidelberg University)Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization Aseem Baranwal (University of Waterloo) · Kimon Fountoulakis (University of Waterloo) · Aukosh Jagannath (University of Waterloo)Breaking the Limits of Message Passing Graph Neural Networks Muhammet Balcilar (Université de Rouen - LITIS) · Pierre Heroux (University of Rouen Normandy) · Benoit Gauzere (INSA Rouen) · Sebastien Adam (Université de Rouen Normandie) · Paul Honeine (LITIS Lab, Université de Rouen Normandie) · Pascal Vasseur (LITIS Université de Rouen Normandie) looks very exciting; not available yetFrom Local Structures to Size Generalization in Graph Neural Networks Gilad Yehudai (Weizmann Institute of Science) · Ethan Fetaya (Bar-Ilan University) · eli meirom (NVIDIA) · Gal Chechik (Nvidia) · Haggai Maron (NVIDIA Research) size generalization and d-patternGNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings Matthias Fey (TU Dortmund University) · Jan Eric Lenssen (TU Dortmund) · Frank Weichert (Technical University of Dortmund) · Jure Leskovec (Stanford University) seems size invariant and preserving expressive power by reading abstractDirected Graph Embeddings in Pseudo-Riemannian Manifolds Aaron Sim (BenevolentAI) · Maciej Wiatrak (BenevolentAI) · Angus Brayne (BenevolentAI) · Páidí Creed (BenevolentAI) · Saee Paliwal (Benevolent AI)Interpretable Stability Bounds for Spectral Graph Filters Henry Kenlay (University of Oxford) · Dorina Thanou (Swiss Data Science Center (EPFL and ETH Zurich)) · Xiaowen Dong (University of Oxford)DeepWalking Backwards: From Embeddings Back to Graphs Sudhanshu Chanpuriya (University of Massachusetts Amherst) · Cameron Musco (University of Massachusetts Amherst) · Konstantinos Sotiropoulos (Boston University) · Charalampos Tsourakakis (ISI Foundation, Boston University) Embedding as matrix factorization and an exact factorization algorithm新的GNN结构GRAND: Graph Neural Diffusion Ben Chamberlain (Twitter) · Maria Gorinova (University of Edinburgh) · Michael Bronstein (Twitter) · Stefan Webb (Twitter) · James Rowbottom (Twitter) · Emanuele Rossi (Twitter)How Framelets Enhance Graph Neural Networks Xuebin Zheng (The University of Sydney) · Bingxin Zhou (The University of Sydney) · Junbin Gao (The University of Sydney) · Yuguang Wang (Max Planck Institute for Mathematics in Sciences; Shanghai Jiao Tong University; University of New South Wales) · Pietro Lió (University of Cambridge) · Ming Li (Zhejiang Normal University) · Guido Montufar (UCLA Math / Stat; MPI MIS)Directional Graph Networks Dominique Beaini (InVivo AI) · Saro Passaro (University of Cambridge) · Vincent Létourneau (Université de Ottawa) · Will Hamilton (McGill University and Mila) · Gabriele Corso (University of Cambridge) · Pietro Lió (University of Cambridge)Training Graph Neural Networks with 1000 Layers Guohao Li (KAUST) · Matthias Müller (Intel Labs) · Bernard Ghanem (KAUST) · Vladlen Koltun (Intel Labs) exciting results; reversible connections, group convolutions, weight tying, and equilibrium models to advance the memory and parameter efficiencyGraph Mixture Density NetworksFederico Errica (University of Pisa) · Davide Bacciu (University of Pisa) · Alessio Micheli (Universita di Pisa)E(n) Equivariant Graph Neural Networks Víctor Garcia Satorras (University of Amsterdam) · Emiel Hoogeboom (University of Amsterdam) · Max Welling (University of Amsterdam & Qualcomm)Lipschitz normalization for self-attention layers with application to graph neural networks George Dasoulas (Ecole Polytechnique, Paris, France) · Kevin Scaman (Noah's Ark, Huawei Technologies) · Aladin Virmaux (Huawei)GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings Matthias Fey (TU Dortmund University) · Jan Eric Lenssen (TU Dortmund) · Frank Weichert (Technical University of Dortmund) · Jure Leskovec (Stanford University)Elastic Graph Neural Networks Xiaorui Liu (Michigan State University) · Wei Jin (Michigan State University) · Yao Ma (Michigan State University) · Yaxin Li (Michigan State University) · Hua Liu (Shandong University ) · Yiqi Wang (Michigan State University) · Ming Yan (Michigan State University) · Jiliang Tang (Michigan State University)Size-Invariant Graph Representations for Graph Classification Extrapolations Beatrice Bevilacqua (Purdue University) · Yangze Zhou (Purdue University) · Bruno Ribeiro (Purdue University)Graph Neural Networks Inspired by Classical Iterative Algorithms Yang Yongyi (Fudan University) · Tang Liu (Fudan University) · Yangkun Wang (SJTU) · Jinjing Zhou (Amazon) · Quan Gan (Amazon) · Zhewei Wei (Renmin University of China) · Zheng Zhang (Amazon) · Zengfeng Huang (Fudan University) · David Wipf (Microsoft Research) unrolled proximal algorithms as GNNsGNN与组合优化Deep Latent Graph Matching Tianshu Yu (Arizona State University) · Runzhong Wang (Shanghai Jiao Tong University) · Junchi Yan (Shanghai Jiao Tong University) · baoxin Li (Arizona State University)On Explainability of Graph Neural Networks via Subgraph Explorations Hao Yuan (Texas A&M University) · Haiyang Yu (Texas A&M University) · Jie Wang (University of Science and Technology of China) · Kang Li (Rutgers) · Shuiwang Ji (Texas A&M University) explanation as combinatorial optimization over graph如何更好地训练GNNGraphNorm: A Principled Approach to Accelerating Graph Neural Network Training Tianle Cai (Princeton University) · Shengjie Luo (Peking University) · Keyulu Xu (MIT) · Di He (Microsoft Research) · Tie-Yan Liu (Microsoft Research Asia) · Liwei Wang (Peking University)Self-supervised Graph-level Representation Learning with Local and Global Structure Minghao Xu (Shanghai Jiao Tong University) · Hang Wang (Shanghai Jiao Tong University) · Bingbing Ni (Shanghai Jiao Tong University) · Hongyu Guo (National Research Council Canada) · Jian Tang (HEC Montreal & MILA)Memory-Efficient Graph Neural Networks Guohao Li (KAUST) · Matthias Müller (Intel Labs) · Bernard Ghanem (KAUST) · Vladlen Koltun (Intel Labs) exciting results; idea seems like neural ode by only reading abstract?Graph Contrastive Learning Automated Yuning You (Texas A&M University) · Tianlong Chen (University of Texas at Austin) · Yang Shen (Texas A&M University) · Zhangyang Wang (University of Texas at Austin)From Local Structures to Size Generalization in Graph Neural Networks Gilad Yehudai (Weizmann Institute of Science) · Ethan Fetaya (Bar-Ilan University) · eli meirom (NVIDIA) · Gal Chechik (Nvidia) · Haggai Maron (NVIDIA Research) new self-supervised taskImproving Breadth-Wise Backpropagation in Graph Neural Networks helps Learning Long-Range Dependencies. Denis Lukovnikov (University of Bonn) · Asja Fischer (Ruhr University Bochum)Automated Graph Representation Learning with Hyperparameter Importance Explanation Xin Wang (Tsinghua University) · Shuyi Fan (Tsinghua University) · Kun Kuang (Tsinghua University) · wenwu zhu (Tsinghua University)GNN ExtrapolationGraph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization Aseem Baranwal (University of Waterloo) · Kimon Fountoulakis (University of Waterloo) · Aukosh Jagannath (University of Waterloo)From Local Structures to Size Generalization in Graph Neural Networks Gilad Yehudai (Weizmann Institute of Science) · Ethan Fetaya (Bar-Ilan University) · eli meirom (NVIDIA) · Gal Chechik (Nvidia) · Haggai Maron (NVIDIA Research) new self-supervised taskGNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings Matthias Fey (TU Dortmund University) · Jan Eric Lenssen (TU Dortmund) · Frank Weichert (Technical University of Dortmund) · Jure Leskovec (Stanford University)Size-Invariant Graph Representations for Graph Classification Extrapolations Beatrice Bevilacqua (Purdue University) · Yangze Zhou (Purdue University) · Bruno Ribeiro (Purdue University)GNN可解释性On Explainability of Graph Neural Networks via Subgraph Explorations Hao Yuan (Texas A&M University) · Haiyang Yu (Texas A&M University) · Jie Wang (University of Science and Technology of China) · Kang Li (Rutgers) · Shuiwang Ji (Texas A&M University) explanation as combinatorial optimization over graphGenerative Causal Explanations for Graph Neural Networks Wanyu LIN (University of Toronto) · Hao Lan (University of Toronto) · Baochun Li (University of Toronto) explanation as graph generationImproving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity Ryan Henderson (Bayer) · Djork-Arné Clevert (Bayer AG) · Floriane Montanari (Bayer AG)图生成GraphDF: A Discrete Flow Model for Molecular Graph Generation Youzhi Luo (Texas A&M University) · Keqiang Yan (Texas A&M University, College Station) · Shuiwang Ji (Texas A&M University)Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation Xiaohui Chen (Tufts University) · Xu Han (Tufts University) · Jiajing Hu (Tufts University) · Francisco R Ruiz (DeepMind) · Liping Liu (Tufts University)对抗攻击Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks Xin Zhao (Auburn University) · Zeru Zhang (Auburn University) · Zijie Zhang (Auburn University) · Lingfei Wu (IBM Research AI) · Jiayin Jin (Auburn University) · Yang Zhou (Auburn University) · Ruoming Jin (Kent State University) · Dejing Dou (" University of Oregon, USA") · Da Yan (University of Alabama at Birmingham)Information Obfuscation of Graph Neural Networks Peiyuan Liao (Carnegie Mellon University) · Han Zhao (University of Illinois at Urbana-Champaign) · Keyulu Xu (MIT) · Tommi Jaakkola (MIT) · Geoff Gordon (Carnegie Mellon University) · Stefanie Jegelka (Massachusetts Institute of Technology) · Ruslan Salakhutdinov (Carnegie Mellen University) fundamental tradeoffs in robustness and accuracyIntegrated Defense for Resilient Graph Matching Jiaxiang Ren (Auburn University) · Zijie Zhang (Auburn University) · Jiayin Jin (Auburn University) · Xin Zhao (Auburn University) · Sixing Wu (Peking University) · Yang Zhou (Auburn University) · Yelong Shen (Microsoft Dynamics 365 AI) · Tianshi Che (Auburn University) · Ruoming Jin (Kent State University) · Dejing Dou (" University of Oregon, USA")GNN与物理Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks Eli Meirom (NVIDIA Research) · Haggai Maron (NVIDIA Research) · Shie Mannor (Technion) · Gal Chechik (NVIDIA / Bar-Ilan University)GNN与时间序列Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting Yuzhou Chen (Southern Methodist University) · Ignacio Segovia (University of Texas at Dallas) · Yulia R Gel (University of Texas at Dallas)GNN与auto-MLAutomated Graph Representation Learning with Hyperparameter Importance Explanation Xin Wang (Tsinghua University) · Shuyi Fan (Tsinghua University) · Kun Kuang (Tsinghua University) · wenwu zhu (Tsinghua University)看标题没看懂咋分类的World Model as a Graph: Learning Latent Landmarks for Planning Lunjun Zhang (University of Toronto) · Ge Yang (University of Chicago) · Bradly Stadie (Vector Institute)
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