✨ Interested in graph neural networks and computational biology, I collected papers from the top conference.
Neural Structured Prediction for Inductive Node Classification ,Meng Qu, Huiyu Cai, Jian Tang, paper
(Contrastive)Contrastive Label Disambiguation for Partial Label Learning ,Haobo Wang, Ruixuan Xiao, Sharon Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao, paper
Frame Averaging for Invariant and Equivariant Network Design, Omri Puny, Matan Atzmon, Edward J. Smith, Ishan Misra, Aditya Grover, Heli Ben-Hamu, Yaron Lipman, paper
(CLIP)Poisoning and Backdooring Contrastive Learning, Nicholas Carlini, Andreas Terzis, paper
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?", Asiri Wijesinghe, Qing Wang, paper
Expressiveness and Approximation Properties of Graph Neural Networks, Floris Geerts, Juan L Reutter, paper
Understanding over-squashing and bottlenecks on graphs via curvature, Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein, paper
(Bio)Data-Efficient Graph Grammar Learning for Molecular Generation, Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik, paper
(Bio)GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation, Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang, paper
(Bio)Spanning Tree-based Graph Generation for Molecules, Sungsoo Ahn, Binghong Chen, Tianzhe Wang, Le Song, paper
Equivariant Subgraph Aggregation Networks, Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron, paper
(Bio)Geometric and Physical Quantities improve E(3) Equivariant Message Passing, Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, Max Welling, paper
(Contrastive)Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption, Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler, paper
Self-supervised Learning is More Robust to Dataset Imbalance, Hong Liu, Jeff Z. HaoChen, Adrien Gaidon, Tengyu Ma,paper
(Bio)Equivariant Transformers for Neural Network based Molecular Potentials, Philipp Thölke, Gianni De Fabritiis, paper
(Bio)Unifying Likelihood-free Inference with Black-box Optimization and Beyond, Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville, paper
(ODE)Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks, Marten Lienen, Stephan Günnemann, paper
(Bio)Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design, Wengong Jin, Jeremy Wohlwend, Regina Barzilay, Tommi S. Jaakkola, paper
(Bio)Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design, Wenhao Gao, Rocío Mercado, Connor W. Coley, paper
Convergent Boosted Smoothing for Modeling GraphData with Tabular Node Features, Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf, paper
(Bio)Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking, Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause, paper
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions, Leslie O'Bray, Max Horn, Bastian Rieck, Karsten Borgwardt, paper
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions, Nicholas Gao, Stephan Günnemann, paper
Do We Need Anisotropic Graph Neural Networks?, Shyam A. Tailor, Felix Opolka, Pietro Lio, Nicholas Donald Lane, paper
(EEG)Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis, Siyi Tang, Jared Dunnmon, Khaled Kamal Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel Rubin, Christopher Lee-Messer, paper
(Contrastive)Incremental False Negative Detection for Contrastive Learning, Tsai-Shien Chen, Wei-Chih Hung, Hung-Yu Tseng, Shao-Yi Chien, Ming-Hsuan Yang, paper
Handling Distribution Shifts on Graphs: An Invariance Perspective, Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf, paper
(Bio)Energy-Inspired Molecular Conformation Optimization, Jiaqi Guan, Wesley Wei Qian, qiang liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng, paper
Equivariant Graph Mechanics Network With Constraints, Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang, paper
Multi-view Contrastive Graph Clustering ,ErLin Pan, Zhao Kang,NeurIPS2021,paper
Contrastive Laplacian Eigenmaps,Hao Zhu, Ke Sun, Peter Koniusz,NeurIPS2021 paper
Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, Chen Gong,NeurIPS2021,paper
Adversarial Graph Augmentation to Improve Graph Contrastive Learning,Susheel Suresh, Pan Li, Cong Hao, Jennifer Neville,NeurIPS2021,paper
Directed Graph Contrastive Learning,Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, Changhu Wang,NeurIPS2021,paper
Disentangled Contrastive Learning on Graphs,Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu,NeurIPS2021,paper
InfoGCL: Information-Aware Graph Contrastive Learning,Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang,NeurIPS2021,paper
Contrastive and Generative Graph Convolutional Networks for Graph-Based SemiSupervised Learning,Sheng Wan,Shirui Pan,Jian Yang,Chen Gong,AAAI2021,paper
Contrastive Self-Supervised Learning for Graph Classification,Jiaqi Zeng, Pengtao Xie,AAAI2021,paper
Graph Contrastive Learning Automated,Yuning You,Tianlong Chen,Yang Shen,Zhangyang Wang,ICML2021,paper
Self-supervised Graph-level Representation Learning with Local and Global Structure,ICML2021paper
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks IJCAI2021,paper
Graph Debiased Contrastive Learning with Joint Representation Clustering,IJCAI2021paper
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning,IJCAI2021paper
CuCo: Graph Representation with Curriculum Contrastive Learning,IJCAI2021paper