ICML-2021图相关论文汇总

数据统计

数据统计转自博主Sergey Ivanov，几个有意思的统计如下：

不同大学的paper分布

不同机构的paper分布

中国机构的paper分布

美国机构的paper分布

全截图

图论文清单

官网链接：https://icml.cc/Conferences/2021/AcceptedPapersInitial

Two Heads are Better Than One: Hypergraph-Enhanced Graph Reasoning for Visual Event Ratiocination Wenbo Zheng (School of Software Engineering, Xi’an Jiaotong University）, Lan Yan （The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences）, Chao Gou (School of Intelligent Systems Engineering, Sun Yat-sen University）, Fei-Yue Wang (The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences）
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）
GRAND: Graph Neural Diffusion Ben Chamberlain (Twitter）, Maria Gorinova (University of Edinburgh）, Michael Bronstein (Twitter）, Stefan Webb (Twitter）, James Rowbottom (Twitter）, Emanuele Rossi (Twitter）
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）
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph Gen Li (Tsinghua University, China）, Yuantao Gu (Tsinghua University）
Differentially Private Densest Subgraph Detection Dung Nguyen (University of Virginia）, Anil Vullikanti (Biocomplexity Institute and Dept of Computer Science, University of Virginia）
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）
Generative Causal Explanations for Graph Neural Networks Wanyu LIN (University of Toronto）, Hao Lan (University of Toronto）, Baochun Li (University of Toronto）
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）
GraphNorm: 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）
Let’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）
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）
GLSearch: Maximum Common Subgraph Detection via Learning to Search Yunsheng Bai (UCLA）, Derek Xu (University of California, Los Angeles）, Yizhou Sun (UCLA）, Wei Wang (UCLA）
Compositional Video Synthesis with Action Graphs Amir Bar (Tel Aviv University）, Roi Herzig (Tel Aviv University/ UC Berkeley） , Xiaolong Wang (UCSD）, Anna Rohrbach (UC Berkeley）, Gal Chechik (NVIDIA / Bar-Ilan University）, Trevor Darrell (University of California at Berkeley）, Amir Globerson (Tel Aviv University, Google）
Memory-Efficient Graph Neural Networks Guohao Li (KAUST）, Matthias Müller (Intel Labs）, Bernard Ghanem (KAUST）, Vladlen Koltun (Intel Labs）
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）
Flow-based Attribution in Graphical Models: A Recursive Shapley Approach Raghav Singal (Amazon）, George Michailidis (University of Florida）, Hoiyi Ng (Amazon）
Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information Changhun Jo (University of Wisconsin-Madison）, Kangwook Lee (UW Madison）
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）
World Model as a Graph: Learning Latent Landmarks for Planning Lunjun Zhang (University of Toronto）, Ge Yang (University of Chicago）, Bradly Stadie (Vector Institute）
Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch） Hunter Lang (MIT）, David Sontag (Massachusetts Institute of Technology）, Aravindan Vijayaraghavan (Northwestern University）
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）
Graph Mixture Density Networks Federico Errica (University of Pisa）, Davide Bacciu (University of Pisa）, Alessio Micheli (Universita di Pisa）
Automated Graph Representation Learning with Hyperparameter Importance Explanation Xin Wang (Tsinghua University）, Shuyi Fan (Tsinghua University）, Kun Kuang (Tsinghua University）, wenwu zhu (Tsinghua University）
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）
Skill Discovery for Exploration and Planning using Deep Skill Graphs Akhil Bagaria (Brown University）, Jason Senthil (Brown University）, George Konidaris (Brown）
E(n） Equivariant Graph Neural Networks Víctor Garcia Satorras (University of Amsterdam\）, Emiel Hoogeboom (University of Amsterdam）, Max Welling (University of Amsterdam & Qualcomm）
LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs Hongyu Ren (Stanford University）, Hanjun Dai (Google Brain）, Bo Dai (Google Brain） , Xinyun Chen (UC Berkeley）, Michihiro Yasunaga (Stanford University）, Haitian Sun (Google）, Dale Schuurmans (Google / University of Alberta）, Jure Leskovec (Stanford University）, Denny Zhou (Google Brain）
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）
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing Kaixin Wang (National University of Singapore）, Kuangqi Zhou (National University of Singapore）, Qixin Zhang (city university of hong kong）, Jie Shao ( Fudan University）, Bryan Hooi (National University of Singapore）, Jiashi Feng (National University of Singapore）
Spectral vertex sparsifiers and pair-wise spanners over distributed graphs Chunjiang Zhu (University of North Carolina Greensboro）, Qinqing Liu (University of Connecticut）, Jinbo Bi (University of Connecticut）
SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples Anshoo Tandon (National University of Singapore）, Aldric Han (National University of Singapore）, Vincent Tan (National University of Singapore）
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）
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）
Online Graph Dictionary Learning Cédric Vincent-Cuaz (INRIA Sophia Antipolis）, Titouan Vayer (IRISA）, Rémi Flamary (École Polytechnique）, Marco Corneli (Université Côte d’Azur）, Nicolas Courty (UBS）
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）
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning Muhammad Arrasy Rahman (The University of Edinburgh）, Niklas Hopner (University of Amsterdam）, Filippos Christianos (University of Edinburgh）, Stefano Albrecht (University of Edinburgh）
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）
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）
Local Graph Algorithms for Learning Higher-Order Structures Peter Macgregor (The University of Edinburgh）, He Sun (University of Edinburgh）
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity Ryan Henderson (Bayer）, Djork-Arné Clevert (Bayer AG）, Floriane Montanari (Bayer AG）
Directed 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）
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies Paul Vicol (University of Toronto）, Luke Metz (Google Brain）, Jascha Sohl-Dickstein (Google Brain）
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）
Stochastic Iterative Graph Matching Linfeng Liu (Tufts University）, Michael C. Hughes (Harvard University）, Soha Hassoun (）, Liping Liu (Tufts University）
Improving Breadth-Wise Backpropagation in Graph Neural Networks helps Learning Long-Range Dependencies. Denis Lukovnikov (University of Bonn）, Asja Fischer (Ruhr University Bochum）
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）
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）
Context-Aware Online Collective Inference for Templated Graphical Models Charles Dickens (UCSC）, Connor F Pryor (UCSC）, Eriq Augustine (University of California, Santa Cruz）, Alexander Miller (UCSC）, Lise Getoor (University of California Santa Cruz）
Integrated 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"）
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）
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）
Hierarchical Agglomerative Graph Clustering in Nearly Linear Time Laxman Dhulipala (MIT CSAIL）, David Eisenstat (Google）, Jakub Łącki (Google）, Vahab Mirrokni (Google Research）, Jessica Shi (MIT）
Size-Invariant Graph Representations for Graph Classification Extrapolations Beatrice Bevilacqua (Purdue University）, Yangze Zhou (Purdue University）, Bruno Ribeiro (Purdue University）
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More Johannes Klicpera (Technical University Munich）, Marten Lienen (Technical University of Munich）, Stephan Günnemann (Technical University of Munich）
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）