**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.

**Fresh picks from ArXiv**

This week on ArXiv: tricks to improve GNNs, unlearning problem on graphs, and cheating on TOEFL with GNNs

**✍️**If I forgot to mention your paper, please shoot me a message and I will update the post.

GNNs

* A nonlinear diffusion method for semi-supervised learning on hypergraphs with Austin R. Benson

* Bag of Tricks of Semi-Supervised Classification with Graph Neural Networks

* Self-supervised Graph Neural Networks without explicit negative sampling

* Graph Unlearning

* InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?

* Beyond permutation equivariance in graph networks

* Knowledge-aware Contrastive Molecular Graph Learning

* Autism Spectrum Disorder Screening Using Discriminative Brain Sub-Networks: An Entropic Approach

Survey

* A Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning

**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.

YouTube

Graph Neural Networks User Group Meeting on Mar 25, 2021

Agenda4:00 - 4:30 (PST): Therapeutics Data Commons: Machine Learning Datasets and Tasks for TherapeuticsAbstract: Machine learning (ML) for therapeutics is a...

**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.

YouTube

PyTorch Geometric tutorial: Graph Autoencoders & Variational Graph Autoencoders

In this tutorial, we present Graph Autoencoders and Variational Graph Autoencoders from the paper:https://arxiv.org/pdf/1611.07308.pdfLater, we show an examp...

**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

Telegram

Graph Machine Learning

Graph Machine Learning research groups: Yaron Lipman

I do a series of posts on the groups in graph research, previous post is here. The 25th is Yaron Lipman, a professor in Israel, who has been co-authoring many papers on equivariances and the power of GNNs.…

I do a series of posts on the groups in graph research, previous post is here. The 25th is Yaron Lipman, a professor in Israel, who has been co-authoring many papers on equivariances and the power of GNNs.…

**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.

YouTube

Graph Deep Learning 2021 - Kyle Cranmer - GNNs in physics

This guest lecture was part of the 2021 Graph Deep Learning course at Università della Svizzera italiana (https://www.usi.ch/). Prof. Cranmer talks about how...

**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.

Substack

GML Subscribers - Open Research Problems in graph ML community

"Exploration is curiosity put into action."

**Fresh picks from ArXiv**

This week on ArXiv: new heterophily datasets, improved inference and expressive power for GNNs

**

**🦹**If I forgot to mention your paper, please shoot me a message and I will update the post.

GNNs

* New Benchmarks for Learning on Non-Homophilous Graphs

* Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions with Pietro Liò

* Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm

* Sub-GMN: The Subgraph Matching Network Model

* SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network

Math

* Using Graph Theory to Derive Inequalities for the Bell Numbers

Software

* LAGraph: Linear Algebra, Network Analysis Libraries, and the Study of Graph Algorithms

Survey

* Scene Graphs: A Survey of Generations and Applications

**Topological GNNs and graph models for video**

Two articles recently were featured on Synced. One is Making GNNs ‘Topology-Aware’ to Advance their Expressive Power about using persistent homology for additional expressivity of GNNs. Another is SOTA GNN ‘Reasons’ Interactions over Time to Boost Video Understanding about modeling images as graphs to reason about their content.

Synced | AI Technology & Industry Review

Making GNNs ‘Topology-Aware’ to Advance their Expressive Power: New Paper from ETH, SIB & KU Leuven | Making GNNs 'Topology-Aware'…

A research team from ETH Zurich, SIB Swiss Institute of Bioinformatics, and KU Leuven proposes Topological Graph Layer (TOGL), a new type of graph neural network layer capable of leveraging the multi-scale topological information of input graphs.Graph neural…

**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.

LOGML 2021

Geometry and Machine Learning | LOGML 2021

LOGML 2021 will be held 12-16 July 2021 online. During the week, early career researchers such as Ph.D. students will work in small groups under the guidance of experienced mentors on a research project in the intersection of geometry and machine learning.

**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

Open Graph Benchmark

Leaderboards for Node Property Prediction

Check leaderboards for - ogbn-products - ogbn-proteins - ogbn-arxiv - ogbn-papers100M - ogbn-mag

**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.

Quanta Magazine

Mathematicians Settle Erdős Coloring Conjecture

Fifty years ago, Paul Erdős and two other mathematicians came up with a graph theory problem that they thought they might solve on the spot. A team of mathematicians has finally settled it.

**Fresh picks from ArXiv**

This week on ArXiv: improved power of GNNs, new autoML library for graphs, and decreasing query time for graph traversal

**🕔**If I forgot to mention your paper, please shoot me a message and I will update the post.

GNNs

* Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels

* A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs

* Learning to Coordinate via Multiple Graph Neural Networks

* DyGCN: Dynamic Graph Embedding with Graph Convolutional Network

* Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs with Philip S. Yu

* The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks

kNN

* Graph Reordering for Cache-Efficient Near Neighbor Search with Alex Smola

Software

* AutoGL: A Library for Automated Graph Learning

Survey

* Representation Learning for Networks in Biology and Medicine: Advancements, Challenges, and Opportunities with Marinka Zitnik

**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.

oddworkshop.github.io

ODD 2021 - 6th Outlier Detection and Description Workshop

ODD 2021, 6th Outlier Detection and Description Workshop, co-located with KDD 2021, Virtual

**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.

**Videos from CS224W**

A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021. Promised to be 2 lectures each week. Slides available on the site too (homeworks are still missing).

YouTube

CS224W: Machine Learning with Graphs | 2021 | Lecture 1.1 - Why Graphs

Jure LeskovecComputer Science, PhDGraphs are a general language for describing and analyzing entities with relations/interactions. There are many types of ne...

**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

**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.

GitHub

naganandy/graph-based-deep-learning-literature

links to conference publications in graph-based deep learning - naganandy/graph-based-deep-learning-literature