Graph Machine Learning
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Everything I (Sergey Ivanov) want to share about graph theory, computer science, machine learning, etc.

If you have something worth sharing with the community, reach out @ivanovserg990
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GNN Explainer UI

Awesome tool that provides user interface for visualizing edge attributions on trained GNN models and compare different explanation methods. An explanation method takes as input a GNN model and a single sample graph and outputs attribution values for all the edges in the graph. Each explanation method uses a different approach for calculating how important each edge is and it is important to evaluate explanation methods as well.
Video and slides: GNN User Group meeting 3

In the third meeting of GNN user group, there are two talks:
* Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics by Marinka Zitnik and Kexin Huang (Harvard)
* The Transformer Network for TSP by Xavier Bresson (NTU)

Slides are available in their slack channel.
Pytorch Geometric tutorial

Awesome tutorials on how to program your GNNs with PyTorch Geometric. I often say that the best way to learn about GNNs is through coding, so if you are new I would definitely recommend checking it out. There are upcoming sessions soon, if you want to do it live.
Graph Machine Learning research groups: Mingyuan Zhou

I do a series of posts on the groups in graph research, previous post is here. The 26th is Mingyuan Zhou, a professor at the University of Texas, who has been working on statistical aspects of GNNs.

Mingyuan Zhou (~1985)
- Affiliation: The University of Texas at Austin
- Education: Ph.D. at Duke University in 2013 (advisors: Lawrence Carin)
- h-index 30
- Interests: hyperbolic graph embeddings, bayesian GNNs, graph auto-encoders
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.
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.
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.
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.
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
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.
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.
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.
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.
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.