Graph Machine Learning

Recent approaches to Graph Convolutional Networks, Graph Representation Learning and Reinforcement Learning Surprisingly discovered a local workshop on GML with strong list of keynote speakers. Free of charge 🤫 https://gcn-grl-rl.sciencesconf.org/

Reinforcement Learning for Combinatorial Optimization: A Survey Our new submission to IJCAI survey track. We surveyed all of the literature we found on applying RL methods for combinatorial optimization problems (e.g. TSP, Knapsack, MaxCut). There are three types of the RL approaches we categorized the papers: Value-based, Policy-based, and Monte-Carlo Tree Search based. This is one of the domains that appeared very recently, a few years ago, and has an increasing number of successful applications to traditional problems each year. I would say it's a good topic for a fresh Ph.D. student to start working on.

Fresh picks from ArXiv More ICML and KDD submissions and large body on mathematical graph theory 📖 ICML Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization Neural Networks on Random Graphs Embedding Graph Auto-Encoder with Joint Clustering via Adjacency Sharing Adaptive Graph Auto-Encoder for General Data Clustering Computationally Tractable Riemannian Manifolds for Graph Embeddings Set2Graph: Learning Graphs From Sets Node Masking: Making Graph Neural Networks Generalize and Scale Better Deep Graph Mapper: Seeing Graphs through the Neural Lens Learning Dynamic Knowledge Graphs to Generalize on Text-Based Games by Microsoft and group of William L. Hamilton Learning to Simulate Complex Physics with Graph Networks by Deepmind + group of Jure Leskovec KDD Self-Enhanced GNN: Improving Graph Neural Networks UsingModel Outputs Graph4Code: A Machine Interpretable Knowledge Graph for Code Localized Flow-Based Clustering in Hypergraphs by group of Jon Kleinberg WWW Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction Graph Theory Building large k-cores from sparse graphs Distributed graph problems through an automata-theoretic lens Computing the k Densest Subgraphs of a Graph Seeing Far vs. Seeing Wide: Volume Complexity of Local Graph Problems Planar graphs have bounded queue-number Review Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

Graph ML Surveys A good way to start in this domain is to read what people already have done. Videos * Learning on Non-Euclidean Domains * Stanford Course CS 224w GNN * Graph Neural Networks: A Review of Methods and Applications 2018 * A Comprehensive Survey on Graph Neural Networks 2019 * A Gentle Introduction to Deep Learning for Graphs 2019 * Deep Learning on Graphs: A Survey 2018 * Relational inductive biases, deep learning, and graph networks 2018 * Geometric deep learning: going beyond Euclidean data 2016 * Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview 2019 * Machine Learning on Graphs: A Model and Comprehensive Taxonomy 2020 Graph kernels * A Survey on Graph Kernels 2019 * Graph Kernels: A Survey 2019 Adversarial Attacks * Adversarial Attack and Defense on Graph Data: A Survey 2018 Representation Learning * Learning Representations of Graph Data -- A Survey 2019 * Representation Learning on Graphs: Methods and Applications 2017 * Representation Learning for Dynamic Graphs: A Survey 2020 JMLR Books Graph Representation Learning Book by Will Hamilton Deep Learning on Graphs by Yao Ma and Jiliang Tang

Visualization of small graphs and corresponding statistics. https://dominikschmidt.xyz/spectral-clustering-exp/

Ringel’s conjecture is proved. Ringel's conjecture states that every complete graph with 2n+1 nodes can be decomposed into a set of any identical non-overlapping trees of order n. In other words, take any tree with n nodes, place it on the complete graph with 2n+1 nodes, remove the edges your tree covers, and continue with the remaining graph. No matter which tree you have started with, there is a procedure to remove all the edges in a complete graph by placing your tree step by step. This conjecture was known for 60 years and finally has been proved last month. At last this article makes a good job explaining how it was done.

NeurIPS 2019 stats 6743 number of submissions 1428 accepted 21% acceptance rate 75 graph papers (5% of accepted)

Do Deep Graph Neural Networks exist? One of the open questions in GNN literature is whether deep GNN, i.e. GNN with many layers (e.g. more than 10), is useful. There is a theoretical paper, What graph neural networks cannot learn: depth vs width, that proves that at least the number of layers * the embedding size of each layer should be proportional to the number of the nodes in the graph if GNN can compute many Turing computable functions. So if a graph has 10K nodes, then d*w = O(10K). For example, common embedding size, w, is 128 or 256, which means that a number of layers should be 40. There is a cost associated with each layer: each node has to look at every neighbor and aggregate its information. So most of the implementations have up to 5 layers for obvious reasons, it's very time-consuming to compute. Somewhat contrary, another theoretical paper, Graph Neural Networks Exponentially Lose Expressive Power for Node Classification, shows that under the certain conditions on the graph, GNN will essentially carry only degree information for each node, which is the most local property you can have for a node. This does not contradict the previous paper as (1) this paper works in a limit, (2) previous paper says that if d*w < O(n) then there is an instance of a graph for which GNN fails, which does not mean the result is universal for all graphs, and (3) this paper has certain conditions to hold which are only applicable to a narrow family of graphs. Beyond this, there is a question of double descent, whether it occurs in GNN setting, which is yet the next question to solve. So, my response is that for now we still have little understanding if deep GNN is useful and if so, how we can make them efficient in practice.

Fresh picks from ArXiv ICML and KDD 20 submissions, AISTATS 20, Graph Isomorphism, and Review ICML 20 submissions Graph Convolutional Gaussian Processes For Link Prediction When Labelled Data Hurts: Deep Semi-Supervised Classification with the Graph 1-Laplacian Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling Differentiable Graph Module (DGM) for Graph Convolutional Network by group of Michael Bronstein Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models Towards Similarity Graphs Constructed by Deep Reinforcement Learning by Yandex team Connectivity-driven Communication in Multi-agent Reinforcement Learning through Diffusion Processes on Graphs Explainable Deep Modeling of Tabular Data using TableGraphNet Graph Filtration Learning Graph Prolongation Convolutional Networks Deep Coordination Graphs Unifying Graph Convolutional Neural Networks and Label Propagation by group of Jure Leskovec KDD 20 submissions Disease State Prediction From Single-Cell Data Using Graph Attention Networks Entity Context and Relational Paths for Knowledge Graph Completion by group of Jure Leskovec Theory Generalization and Representational Limits of Graph Neural Networks by group of Tommi Jaakkola Graph Isomorphism A polynomial time parallel algorithm for graph isomorphism using a quasipolynomial number of processors Isomorphism for Random k-Uniform Hypergraphs Review Hypergraphs: an introduction and review

Network Science Institute at Northeastern University networkscienceinstitute.org With the director Albert-László Barabási, the focus is on biological networks, epidemiology, and formation. They also have a YouTube channel with guest presentations on graph theory.

The Knowledge Graph Conference https://www.knowledgegraph.tech/