Graph Machine Learning

Twitter Recsys 2020 Challenge In a new post Michael Bronstein describes top-3 solutions of the recent Twitter RecSys challenge, with the goal of predicting user engagements (like, retweets, etc.) for the future tweets of the users. As expected, top places were using gradient boosting over neural networks, although some used BERT and other language models to build representations of the tweet. It would be interesting to see if we can use the fact that Twitter is a graph for better predictions of the models.

Python Bindings of JGraphT There is a new python binding of popular java library JGraphT, which is exciting news for those who want efficiency when working with graphs (in addition to other recent news Nvidia GPU-accelerated package). JGraphT is a java library that contains very efficient and generic graph data-structures along with a large collection of state-of-the-art algorithms. What's great is that it allows to use easy interface with java bindings across all OS (including Windows) without installing JVM. JGraphT is known for its efficiency, reliability, and large collection of graph algorithms including pagerank, flows, cuts, vertex covers, colorings, isomorphism checking and more.

Fresh picks from ArXiv Today at ArXiv: building graphs from pretrained language models, graph information bottleneck, and quantum entanglement ⚛️ If I forgot to mention your paper, please shoot me a message and I will update the post. Conferences - XLVIN: eXecuted Latent Value Iteration Nets NeurIPS-DeepRL 2020, with Petar Veličković - Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks NeurIPS 2020 - Graph Information Bottleneck NeurIPS 2020, with Jure Leskovec - Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs NeurIPS 2020, with Jure Leskovec - Graph Geometry Interaction Learning NeurIPS 2020 - Rethinking pooling in graph neural networks NeurIPS 2020 - Heterogeneous Hypergraph Embedding for Graph Classification WSDM 2021 - Contextual Heterogeneous Graph Network for Human-Object Interaction Detection ECCV-2020 Graphs - A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing with Ivan Titov - Graph and graphon neural network stability with Alejandro Ribeiro - Language Models are Open Knowledge Graphs - Can entanglement hide behind triangle-free graphs? Survey - Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization

Genesis Therapeutic — a startup working on GNN drug discovery Launched in November 2019 out of Stanford’s Pande Lab, Genesis Therapeutics is researching GNNs and graph generative models in the field of drug discovery. They recently announced their partnership with Genentech, large biotech company, to test their ML platform for pharma.

Graph Machine Learning research groups: Jiliang Tang I do a series of posts on the groups in graph research, previous post is here. The 17th is Jiliang Tang, coauthor of the book "Deep Learning on Graphs". Jiliang Tang (~1974) - Affiliation: Michigan State University - Education: Ph.D. at Arizona State University in 2015 (advisor: Huan Liu) - h-index 56 - Awards: best paper awards KDD, WSDM; Yahoo! awards; Distinguished Withrow Research Award; NSF Career Award - Interests: graph neural networks, network analysis, anomaly detection on graphs

What’s the Difference Between an Ontology and a Knowledge Graph? An easy explanation of the distinction between KGs and ontologies. In short, ontology + data = knowledge graph.

Open Catalyst Project Facebook and CMU launch Open Catalyst Project, which contains the largest dataset for quantum chemistry predictions. The goal is to predict atomic interactions faster than quantum mechanical simulations (DFT), which could be translated as a graph regression task.

CIKM 2020 stats Dates: Oct 19-23 Where: Online Price: €70 Graph papers can be found at paper digest. • 970/397 full/short submissions (vs 1030/470 in 2019) • 193/103 accepted (vs 202/107 in 2019) • 20% / 26% acceptance rate (vs 19/21% in 2019) • ~97 total graph papers (20% of total)

Fresh picks from ArXiv Today at ArXiv: generalization of GNNs, faster graphlet kernels, and sunshine graphs ☀️ If I forgot to mention your paper, please shoot me a message and I will update the post. GNN - Discriminability of Single-Layer Graph Neural Networks with Alejandro Ribeiro - On Size Generalization in Graph Neural Networks with Haggai Maron - Bi-GCN: Binary Graph Convolutional Network - Fast Graph Kernel with Optical Random Features - Scalable Graph Networks for Particle Simulations - Disentangled Dynamic Graph Deep Generation Math - Supercards, Sunshines and Caterpillar Graphs - An Extension of the Birkhoff-von Neumann Theorem to Non-Bipartite Graphs with Vijay V. Vazirani Conferences - Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs EMNLP 2020 - Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks EMNLP - A Graph Representation of Semi-structured Data for Web Question Answering COLING 2020 - STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation CIKM 2020 - Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks COLING 2020

GML YouTube Videos I was pleasantly surprised to see there is YouTube playlist by Zak Jost that covers some aspects of GNNs, including an interview with DeepMind authors for using GNNs for physics.

NeurIPS 2020. Comprehensive analysis of authors, organizations, and countries. This post analyzes what authors and organizations publish at NeurIPS 2020 this December, similar to the analysis I did for ICML 2020. In addition to general insights (that I also found interesting), there are two collaboration graphs that I created, one between affiliations and one between authors. What's exciting is that these two networks are very different from each other, and the graph of authors is actually quite disconnected with lots of small groups of people (of size ~50) and large diameter (25 hops). Could be interesting in the future to understand why it's the case and what are these small groups of people.