Graph Machine Learning

Videos from CS224W A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021. Promised to be 2 lectures each week. Slides available on the site too (homeworks are still missing).

Weisfeiler and Lehman Go Topological: Message Passing Simplical Networks A video presentation (and slides) by Cristian Bodnar & Fabrizio Frasca on a new type of GNNs that defines neighborhoods based on the simplical complexes of a graph. It goes quite deep into the theory with the supporting experiments in graph isomorphism, graph classification, and trajectory disambiguation.

Outlier detection and description workshop at KDD 2021 Graph methods are very popular in detecting fraud as they are capable to distinguish interactions of fraudsters from benign users. There is a big workshop at KDD 2021 about detecting and describing outliers, with a great list of keynote speakers.

Fresh picks from ArXiv This week on ArXiv: improved power of GNNs, new autoML library for graphs, and decreasing query time for graph traversal 🕔 If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels * A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs * Learning to Coordinate via Multiple Graph Neural Networks * DyGCN: Dynamic Graph Embedding with Graph Convolutional Network * Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs with Philip S. Yu * The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks kNN * Graph Reordering for Cache-Efficient Near Neighbor Search with Alex Smola Software * AutoGL: A Library for Automated Graph Learning Survey * Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities with Marinka Zitnik

Mathematicians Settle Erdős Coloring Conjecture Erdős-Faber-Lovász conjecture states that the minimum number of colors necessary to shade the edges of a hypergraphs so that no overlapping edges have the same color is bounded by the number of vertices. After 50 years of research it has finally been resolved.

Bag of Tricks for Semi-Supervised classification There is a nice short paper on tricks employed on improving performance of GNN. The author, Yangkun Wang, from DGL team has a lot of high scoring entries in the OGB leaderboard, so it's worth employing these tricks: they boost performance a bit but do it consistently. The tricks include: * data augmentation * using labels as node features * renormalization of adjacency matrix * novel loss functions * residual connections from the input

The London Geometry and Machine Learning Summer School 2021 A very cool one week school on geometric deep learning, happening online this summer. Early career researchers such as Ph.D. students will work in small groups under the guidance of experienced mentors on a research project. Applications are open until 31 May 2021.

Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions A blog post by Shyam A. Tailor about a simple modification of GCN layer that is both more efficient and more effective than many standard message-passing algorithms.

Topological GNNs and graph models for video Two articles recently were featured on Synced. One is Making GNNs ‘Topology-Aware’ to Advance their Expressive Power about using persistent homology for additional expressivity of GNNs. Another is SOTA GNN ‘Reasons’ Interactions over Time to Boost Video Understanding about modeling images as graphs to reason about their content.

Fresh picks from ArXiv This week on ArXiv: new heterophily datasets, improved inference and expressive power for GNNs 🦹‍ If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * New Benchmarks for Learning on Non-Homophilous Graphs * Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions with Pietro Liò * Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm * Sub-GMN: The Subgraph Matching Network Model * SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network Math * Using Graph Theory to Derive Inequalities for the Bell Numbers Software * LAGraph: Linear Algebra, Network Analysis Libraries, and the Study of Graph Algorithms Survey * Scene Graphs: A Survey of Generations and Applications

Open Research Problems in Graph ML I thought I would make my first subscriber-only post on the open research problems in graph ML. These are the problems that I have thought a lot and think can have a transformational impact not only on this field, but also on the applications of graph models to other areas.

Insights from Physics on Graphs and Relational Bias A great lecture with lots of insights by Kyle Cranmer on the inductive biases involved in physics. Applying GNNs to life science problems is one of the biggest trends for ML and it's exciting to see more and more cool results in this area.