Graph Machine Learning

GNN Paper Explained Looks like a promising YouTube series on graph machine learning, with the first video explaining the GAT paper.

Fresh picks from ArXiv Today at ArXiv: new transformers for graphs, rethinking spectral GNNs, and capsule graph nets 💊 If I forgot to mention your paper, please shoot me a message and I will update the post. Conferences Hierarchical Graph Capsule Network AAAI 2021 A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training AAAI 2021 Enhancing Balanced Graph Edge Partition with Effective Local Search AAAI 2021 Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting AAAI 2021 An Experimental Study of the Transferability of Spectral Graph Networks Workshop AAAI 2021, with Xavier Bresson A Generalization of Transformer Networks to Graphs Workshop AAAI 2021, with Xavier Bresson Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks Workshop AAAI 2021 Applications Deep Reinforcement Learning of Graph Matching A pipeline for fair comparison of graph neural networks in node classification tasks A Note on Graph-Based Nearest Neighbor Search Survey Graph Neural Networks: Taxonomy, Advances and Trends

Machine Learning for Graphs and Sequential Data (MLGS) Awesome course by Stephan Günnemann covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more ⭐

Generalization Bounds of GNN Expressiveness, that is what class of graphs can be represented by GNN, has been extensively studied during the last two years. On the other hand, generalization, i.e. ability to represent correctly unseen graphs is just gaining attention. Here are some papers that study generalization of GNN. - Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks NeurIPS 2020 - Generalization and Representational Limits of Graph Neural Networks ICML 2020 - Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case ICML 2020 - A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks Arxiv Dec 2020

How Knowledge Graphs Will Transform Data Management And Business Nice article that describes how different companies including BenevolentAI are using knowledge graphs and what are the challenges of using them.

Fresh picks from ArXiv Today at ArXiv: application of GNNs to drug discovery, graph construction by Wallmart, and improving expressiveness via more injective functions 😎 If I forgot to mention your paper, please shoot me a message and I will update the post. GNN - Breaking the Expressive Bottlenecks of Graph Neural Networks - Building Graphs at a Large Scale: Union Find Shuffle - Utilising Graph Machine Learning within Drug Discovery and Development with Michael Bronstein - Molecular graph generation with Graph Neural Networks Conferences - GDPNet: Refining Latent Multi-View Graph for Relation Extraction AAAI 2021 - Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation AAAI 2021 - Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation AAAI 2021 - Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation AAAI 2021 - Context-Aware Graph Convolution Network for Target Re-identification AAAI 2021 - Overcoming Catastrophic Forgetting in Graph Neural Networks AAAI 2021 - Bipartite Graph Embedding via Mutual Information Maximization WSDM 2021 - A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings Workshop NeurIPS 2021 - Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction Workshop NeurIPS 2020 Survey - Deep Analysis on Subgraph Isomorphism - The Future is Big Graphs! A Community View on Graph Processing Systems - A Note on Spectral Graph Neural Network

GML Newsletter - Issue #5: Was 2020 a good year for graph research? My new newsletter is out! 🔥 Talking about my predictions for 2020, NeurIPS recordings, ICLR submissions and a few links that you probably have seen already, my friends!

Machine Learning on Knowledge Graphs @ NeurIPS 2020 A timely digest of NeurIPS 2020 by Michael Galkin. He speaks on improvement over Query2Box, how NAS and meta-learning works in KG domain, constructing the queries from the natural language, and several KG datasets. Worth a read!

Deep Graph Networks Reading Group There is a reading group at Bicocca University (Milan, Italy). Next session will happen on Monday, 14th December at 10am (UK time). The paper "HATS a hierarchical graph attention network for stock movement prediction" will be discussed. If you want to join you can get a link by contacting @Sagax_ita or via [email protected].

Graph Machine Learning research groups: Max Welling I do a series of posts on the groups in graph research, previous post is here. The 20th is Max Welling, the head of the Amsterdam Machine Learning Lab. He co-founded a startup Scyfer BV that was acquired by Qualcomm, where he serves as VP of technologies. Max has a diverse research interests, including lately developments in graph machine learning field. Max Welling (1968) - Affiliation: University of Amsterdam, Qualcomm - Education: Ph.D. at Utrecht University in 1998 (advisor: Gerard 't Hooft) - h-index 73 - Awards: ECCV Koenderink Prize, ICML best papers. - Interests: equivariant networks, variational encoders, GNNs.

Privacy-Preserving Deep Learning Over Graphs 60 slides of overview of the emerging field of privacy-preserving GNNs. Could be interesting if you search for a new research topic.

MoleculeKit: Machine Learning Methods for Molecular Property Prediction and Drug Discovery MoleculeKit is a new framework that deals with molecule predictions. It represents molecules as both graphs and sequences and then apply GNN or kernel together with BERT for downstream molecular tasks (predicting properties of nodes or graphs).