Graph Machine Learning

Secrets of the Surface: The Mathematical Vision of Maryam Mirzakhani There is a documentary that you can watch on the life of Maryam Mirzakhani. In 2014, she was awarded the Fields medal for the work in "the dynamics and geometry of Riemann surfaces and their moduli spaces." You can read about her in this article. For the film, you can register here and they will send a link to the Vimeo, which will be available until 19th May.

Introduction to Deep Learning (I2DL) There is a course on deep learning by Technical University of Munich. Recordings, slides, and exercises are available online.

PhD Theses on Graph Machine Learning Here are some PhD dissertations on GML (including mine). Nino Shervashidze:Scalable graph kernels Petar Veličković:The resurgence of structure in deep neural networks Sergei Ivanov:Combinatorial and neural graph vector representations Thomas Kipf:Deep learning with graph-structured representations

Fresh picks from ArXiv It's Tuesday and so it means we look back at the previous week of ArXiv. In today's episode, among most interesting papers, a new knowledge graph for PubMed 💊 and new surveys on graph machine learning and quantum deep learning ✍️ Applications • Building a PubMed knowledge graph • Reinforcement Learning with Feedback Graphs • Predicting gene expression from network topology using graph neural networks • On new record graphs close to bipartite Moore graphs Conferences • Bundle Recommendation with Graph Convolutional Networks SIGIR 20 • TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation SIGIR 20 • Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks IJCAI 20 • Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics ACL 20 Survey • Machine Learning on Graphs: A Model and Comprehensive Taxonomy with Christopher Ré • Comparison and Benchmark of Graph Clustering Algorithms • Advances in Quantum Deep Learning: An Overview

Max Welling Talk GNN I recently thought about what are other types of GNN exist beyond message-passing. I think one of them can be equivariant networks, i.e. neural networks that have permutation-equivariant properties, but I think there are other possible powerful graph models that are yet to be discovered. In this video, Max Welling discusses his recent works on equivariant NNs for meshes and factor GNNs.

Graph Machine Learning research groups: MichaelBronstein I do a series of posts on the groups in graph research. The fifth is Michael Bronstein. He founded a company Fabula AI on detecting fake news in social networks, which was acquired by Twitter. Also, he was a committee member of my PhD defense 🙂 Michael Bronstein (1980) - Affiliation: Imperial College London; Twitter - Education: Ph.D. at Israel Institute of Technology in Israel in 2007 (supervised by Ron Kimmel); - h-index: 61; - Awards: IEEE and IARP fellow, Dalle Molle prize, Royal Society Wolfson Merit award; - Interests: computer graphics, geometrical deep learning, graph neural networks.

Graph Representation Learning for Algorithmic Reasoning Another idea coming more frequently in recent graph papers is to learn particular graph algorithm such as Bellman-Ford or Breadth-First Search, instead of doing node classification or link prediction. Here is a video from WebConf'20 by Petar Veličković (DeepMind) motivating this approach.

KDD 2020: Workshop on Deep Learning on Graphs If you miss ICML deadlines, there is another good workshop for GML at KDD. Deadline: 15 June 5 pages, double-blind

Fresh picks from ArXiv This week presents new graph datasets OGB, accepted papers at ACL and SIGIR and a survey on Winograd challenge 🍇 GNN • Open Graph Benchmark: Datasets for Machine Learning on Graphs with Jure Leskovec • Low-Dimensional Hyperbolic Knowledge Graph Embeddings with group Christopher Ré • Graph Homomorphism Convolution Graph Theory • Independent Set on Pk-Free Graphs in Quasi-Polynomial Time • Tree-depth and the Formula Complexity of Subgraph Isomorphism Conferences • Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection SIGIR 20 • Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward ACL 20 • Bipartite Flat-Graph Network for Nested Named Entity Recognition ACL 20 • LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network ACL 20 • A Review of Winograd Schema Challenge Datasets and Approaches IJCAI 20

ICLR 2020 Recordings All recordings for papers and workshops are now available to everyone!

Videos from Geometric and Relational Deep Learning Workshop Videos are available from the workshop. Two my favorites are: * Peter Battaglia: Learning Physics with Graph Neural Networks [video] * Yaron Lipman: Deep Learning of Irregular and Geometric Data [video]

Thoughts from the first virtual conference I had nice experience from virtual ICLR 2020. Most of the poster sessions were empty, which allowed me to bother authors with questions. Each paper had two slots during the day, so that I can definitely attend it. Chat allowed finding attendees quite easily, something that I had difficulty with real conferences. So it was much more valuable based on the insights that I gained than in real conference. But I didn't present and can understand that other people didn't get what they wanted. By the way organizers, promised to make the portal available to everyone soon. Now, here are some insights from the papers that I gained. 1) Topic on theoretical explanation of GNNs is hot. We now know some problems that can be approximated with GNN, functions that GNN can compute, limitations of GNN. [paper 1, paper 2, paper 3, paper 4] 2) One emerging topic is to teach GNN to learn algorithms, instead of doing classification task. Here be dragons. [paper 1, paper 2] 3) GNN are used to represent programs and equations. So potentially you can prove theorems with it. [paper 1, paper 2, paper 3, paper 4, paper 5]