Collect recent learning materials about GNNs, cutting-edge trends, etc. weekly update from the Graph Machine Learning channel in Telegram or other platforms.โ ๐ ๐
Fresh Picks from Arxiv
- Theory of Graph Neural Networks: Representation and Learning (https://arxiv.org/abs/2204.07697) ft. Stefanie Jegelka.
- LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning (https://arxiv.org/abs/2204.12881)
- PyGOD: A Python Library for Graph Outlier Detection (https://arxiv.org/abs/2204.12095)
- Reinforced Causal Explainer for Graph Neural Networks (https://arxiv.org/abs/2204.11028)
- Graph Neural Network based Agent in Google Research Football (https://arxiv.org/abs/2204.11142)
- GUARD: Graph Universal Adversarial Defense (https://arxiv.org/abs/2204.09803)
- DropMessage: Unifying Random Dropping for Graph Neural Networks (https://arxiv.org/abs/2204.10037)
- Effects of Graph Convolutions in Deep Networks (https://arxiv.org/abs/2204.09297)
- LEARNING HEURISTICS FOR A* (https://arxiv.org/pdf/2204.08938.pdf) ft. Petar Velickovic.
- AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs (https://arxiv.org/abs/2204.11135)
Can graph neural networks understand chemistry?
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A talk by Dominique Beaini on their recent work and the maze analogy for graph representation learning.
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Main content: Covering papers on Principle Neighbourhood Aggregation, Directional GNNs, and Graph Transformers, this talk touches several sub-areas of recent advances in GNN architectures - WL testing and expressivity, positional encodings, anisotropy, spectral techniques, fully connected message passing, etc.
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Videos: YouTube
Fresh Picks from Arxiv - ICLR Workshops Special Edition The past week on GraphML arXiv: Lots and lots of graph ML for drug discovery papers + graph generation, hyper graphs, subgraphs, and more!
๐ Drug Discovery
- Deep Sharpening Of Topological Features For De Novo Protein Design ft. Bruno Correia, Michael Bronstein, Andreas Loukas
- Decoding Surface Fingerprints For Proteinligand Interactions ft. Bruno Correia, Michael Bronstein, Pietro Lio
- Physics-Informed Deep Neural Network For Rigid-Body Protein Docking ft. Bruno Correia, Michael Bronstein
- Evaluating Generalization in GFlowNets for Molecule Design ft. Yoshua Bengio, Michael Bronstein
- Torsional Diffusion for Molecular Conformer Generation ft. Regina Barzilay, Tommi Jakkola
- Graph Anisotropic Diffusion For Molecules ft. Michael Bronstein
๐ธ Graph Generation
- SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators ft. Andreas Loukas
- Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning ft. Mohit Bansal
๐จ GNN Models
- Simplicial Attention Networks ft. Cris Bodnar, Pietro Lio
- Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
- Graph Ordering Attention Networks
- Expressiveness and Approximation Properties of Graph Neural Networks
- Efficient Representation Learning of Subgraphs by Subgraph-To-Node Translation
๐ Applications
- Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum ft. Wenwu Zhu
- Principled inference of hyperedges and overlapping communities in hypergraphs
- Graph Enhanced BERT for Query Understanding ft. Jilian Tang
Fresh Picks from Arxiv The past week on GraphML arXiv: Hypergraph NNs, GNNs are dynamic programmers, latent graph learning, 3D equivariant molecule generation, and a new GNN library for Keras.
โณ Hypergraph Neural Networks:
- Message Passing Neural Networks for Hypergraphs
- Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs ft. Yu Rong.
- Preventing Over-Smoothing for Hypergraph Neural Networks
โ Theory:
- Graph Neural Networks are Dynamic Programmers ft. Petar Veliฤkoviฤ.
- OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks
- Shift-Robust Node Classification via Graph Adversarial Clustering ft. Jiawei Han.
- Mutual information estimation for graph convolutional neural networks
- Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications ft. Michael Bronstein.
๐ Equivariance and 3D Graphs:
- Equivariant Diffusion for Molecule Generation in 3D ft. Max Welling.
- 3D Equivariant Graph Implicit Functions
๐ Libraries and Surveys:
- GNNkeras: A Keras-based library for Graph Neural Networks and homogeneous and heterogeneous graph processing ft. Franco Scarselli.
- Graph Neural Networks in IoT: A Survey
๐จ Applications:
- Graph similarity learning for change-point detection in dynamic networks ft. Xiowen Dong.
- Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment ft. Yizhou Sun.
- A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning
- Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design ft. Pieter Abbeel.