Graph Machine Learning

Pytorch Geometric tutorial: Special Guest: Matthias Fey A recent talk by Matthias Fey, a founder of pytorch geometric library, about the news and future directions of the library. Large-scale graphs, sparse tensors, pytorch lightning, torchscript, and more.

Graph Machine Learning research groups: GalChechik I do a series of posts on the groups in graph research, previous post is here. The 29th is Gal Chechik, a professor at the Gonda Brain research institute and a director of AI at NVIDIA in Israel. Gal Chechik (~1976) - Affiliation: Bar Ilan University, Israel; NVIDIA - Education: Ph.D. at Hebrew University, Israel in 2004 (advisors: Naftali Tishby and Israel Nelken) - h-index 37 - Interests: biological systems, theory of GNNs, equivariant functions. - Awards: best papers at ICML, ISMB; fullbright fellowship, Alon fellowship

Almost Free Inductive Embeddings Out-Perform Trained Graph Neural Networks in Graph Classification in a Range of Benchmarks A nice blog post by Vadym Safronov (in Russian also here) which shows that you can use not-trained GCN to match or exceed performance of end-to-end trained GCN on graph classification benchmarks.

Graph papers at ICML 2021 ICML 2021 papers are announced, here is some analysis on this. There are about 58 graph papers (if I didn't mention your paper, let me know, I'll fix it). The top authors are displayed.

Fresh picks from ArXiv This week on ArXiv: equivariant GNNs to new groups, new metrics for graph similarity, and parsing emotions with GNNs 😢‍ If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * How Attentive are Graph Attention Networks? * Symmetry-driven graph neural networks * Graph Similarity Description: How Are These Graphs Similar? KDD 2021 * SLGCN: Structure Learning Graph Convolutional Networks for Graphs under Heterophily * Linguistic Structures as Weak Supervision for Visual Scene Graph Generation CVPR 2021 * Directed Acyclic Graph Network for Conversational Emotion Recognition ACL 2021 * On the Universality of Graph Neural Networks on Large Random Graphs * Differentially Private Densest Subgraph Detection ICML 2021 Survey * Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future *A Comprehensive Survey on Community Detection with Deep Learning * A Survey of the Bridge Between Combinatorics and Probability

Reinforcement learning for combinatorial optimization: A survey Our work that surveys recent RL methods for solving combinatorial optimization problems is accepted at Computers & Operations Research journal. This is very active field right now and it shows a lot of promise. Traditionally, NP-hard problems such as Traveling Salesman Problem were solved by algorithms, that were designed specifically for each problem. With RL, it's possible to extend the toolbox by learning a function on available data. I really hope that in 10 years from now using ML approaches for combinatorial problems will be a commonplace.

GNN User Group: meeting 5 Fifth meeting of GNN user group will include talks from: * 4:00 - 4:25 (PST): Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration (Mahdi Saleh, TUM). * 4:25 - 4:50 (PST): Optimizing Graph Transformer Networks with Graph-based Techniques (Loc Hoang, University of Texas at Austin) * 4:50 - 5:15 (PST): Encoding the Core Business Entities Using Meituan Brain (Mengdi Zhang, Meituan) * 5:15 - 5:30 (PST): Open Discussion and Networking Please join us today, 27 May! Zoom link in the description.

TechViz - The Data Science Guy A nice YouTube playlist explaining in details many works on graph embeddings.

NAACL-2021 Papers A list of accepted papers to NLP conference NAACL-2021 is available at digest console. There are ~40 graph papers out of 476 papers.

Fresh picks from ArXiv This week on ArXiv: graph embeddings for drug discovery, new largest GNN, and a gym for solving combinatorial problems ⛹️‍ If I forgot to mention your paper, please shoot me a message and I will update the post. Drug discovery * Predicting Potential Drug Targets Using Tensor Factorisation and Knowledge Graph Embeddings * Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery with William L Hamilton Software * Dorylus: Affordable, Scalable, and Accurate GNN Training over Billion-Edge Graphs * GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching Combinatorics * GraphSAT -- a decision problem connecting satisfiability and graph theory * OpenGraphGym-MG: Using Reinforcement Learning to Solve Large Graph Optimization Problems on MultiGPU Systems GNNs * Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks Survey * Federated Graph Learning -- A Position Paper

Mathematicians Answer Old Question About Odd Graphs A new post at Quanta about the work that settles the question (c. 1960s) of the biggest subgraph with all vertices having odd degree within that subgraph.

Graph Machine Learning research groups: YizhouSun I do a series of posts on the groups in graph research, previous post is here. The 28th is Yizhou Sun, a professor at UCLA, who co-authored a book on heterogeneous information networks. Yizhou Sun (~1982) - Affiliation: UCLA - Education: Ph.D. at UIUC in 2012 (advisors: Jiawei Han) - h-index 48 - Interests: heterogeneous information networks, self-supervised learning, community detection - Awards: best research papers at KDD, ASONAM