Graph Machine Learning

Invariant and equivariant layers with applications to GNN, PointNet and Transformers A blog post by Marc Lelarge about invariant and equivariant functions and their relation to the universality and expressivity of GNN. As the main result they show that any invariant/equivariant function on n points can be represented as a sum of functions on each point independently.

GNN User Group Meeting 4 video Video from the 4th meeting of GNN user group that includes talk from me (on GBDT+GNN model) and professor Pan Li on causal anonymous walks for temporal graphs. Slides can be found on DGL slack channel.

Graph Representation Learning for Drug Discovery Slides Slides from Jian Tang of the talk on de novo drug discovery and drug repurposing.

Geometric Deep Learning Book A new book by graph ML experts Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković on geometric deep learning is released. 156 pages on exploring symmetries that unifies different ML neural network architectures. An accompanying post nicely introduces the history of the geometry and its impact on the physics. It's exciting to see a categorization of many ML approaches from the perspective of the group theory.

Videos from WebConf 2021 Videos from WebConf 2021 are available here. Many graph talks on topics such as GNN, graph models, knowledge graphs, graph embeddings, link prediction, and more.

GNN User Group: meeting 4 Fourth meeting of GNN user group will include talks from me (Sergey Ivanov) where I will talk about combination of GBDT and GNNs, and professor Pan Li from Purdue University who will speak about constructing structural features to improve representations in temporal networks. Please join us on Thusday!

Fresh picks from ArXiv This week on ArXiv: accelerated inference for GNNs, graph MLP and graph-augmented sponsored search 🔍 If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * Learnable Online Graph Representations for 3D Multi-Object Tracking * GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing * Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks Conferences * Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding ICDE 2021 * Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning SIGIR 2021 * AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search SIGIR 2021

Graph Neural Networks in Computational Biology Slides from Petar Veličković about his journey on using machine learning algorithms on biological data.

Graph Machine Learning research groups: LemanAkoglu I do a series of posts on the groups in graph research, previous post is here. The 27th is Leman Akoglu, a professor at the Carnegie Mellon University, with interests in detecting anomalies in graphs. Leman Akoglu (~1983) - Affiliation: Carnegie Mellon University - Education: Ph.D. at Duke University in 2012 (advisors: Christos Faloutsos) - h-index 40 - Interests: anomaly detection, graph neural networks - Awards: best research papers at PAKDD, SIAM SDD, ECML PKDD

Awesome graph repos Collections of methods and papers for specific graph topics. Graph-based Deep Learning Literature — Links to Conference Publications and the top 10 most-cited publications, Related workshops, Surveys / Literature Reviews / Books in graph-based deep learning. awesome-graph-classification — A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Awesome-Graph-Neural-Networks — A collection of resources related with graph neural networks.. awesome-graph — A curated list of resources for graph databases and graph computing tools awesome-knowledge-graph — A curated list of Knowledge Graph related learning materials, databases, tools and other resources. awesome-knowledge-graph — A curated list of awesome knowledge graph tutorials, projects and communities. Awesome-GNN-Recommendation — graph mining for recommender systems. awesome-graph-attack-papers — links to works about adversarial attacks and defenses on graph data or GNNs. Graph-Adversarial-Learning — Attack-related papers, Defense-related papers, Robustness Certification papers, etc., ranging from 2017 to 2021. awesome-self-supervised-gnn — Papers about self-supervised learning on GNNs. awesome-self-supervised-learning-for-graphs — A curated list for awesome self-supervised graph representation learning resources. Awesome-Graph-Contrastive-Learning — Collection of resources related with Graph Contrastive Learning.

Self-supervised learning of GNNs Self-supervised learning (SSL) is a paradigm of learning when we have large amounts unlabeled data and we want to get representation of the input which we can use later for the downstream tasks. The difference between unsupervised and self-supervised learning is that unsupervised learning attempts to learn a representation on a single input, while SSL assumes there is a model trained across several inputs. Examples of unsupervised learning on graphs is graph kernels that boil down to counting some statistics on graphs (e.g. motifs) which would represent a graph. Examples of SSL is when you first create multiple views of the same graph (e.g. by permuting the edges) and then train a model to distinguish views of different graphs. DeepWalk, node2vec and other pre-GNN node embeddings are somewhere in between: they are usually applied to a single graph, but the concept could be well applied to learning representations on many graphs as well. There is a recent boom in this area for graphs, so there are some fresh surveys available (here and here) as well as the awesome list of SSL-GNNs.

Fresh picks from ArXiv This week on ArXiv: equivalence of graph matching and GED, hyperbolic GNNs, and de-anon of blockchain 💲 If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks with Philip S. Yu * Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks with Philip S. Yu * Generative Causal Explanations for Graph Neural Networks * DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks * Identity Inference on Blockchain using Graph Neural Network Conferences * MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks IJCNN 2021 * FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks MLSys Workshop 2021 * Search to aggregate neighborhood for graph neural network ICDE 2021 Hyperbolic * A Hyperbolic-to-Hyperbolic Graph Convolutional Network * Lorentzian Graph Convolutional Networks Math * On the unification of the graph edit distance and graph matching problems