Don't crack under pressure

I remember when I was interning at HKUST during my master's years I had a chance to see a motivational presentation for freshmen from one of the tenured professors there. One of the things he was emphasizing is that PhD is stressful experience with lots of uncertainty and you should keep being focused, brave and don't crack under the pressure. Here is a funny story that demonstrates it. A guy realized he has a bug 2 weeks before submitting his PhD thesis and asked MathOverflow community to help him to fix it, luckily it worked out well.

Graph Mining & Learning Workshop NeurIPS 2020

NeurIPS 2020 just started and there is a good workshop by Google Research on graph mining (by Bryan Perozzi and others). To see the videos you need to register for expo but it's free. Then you will have links to videos. There is also a slide deck with 312 pages about many interesting topics.

Update: it seems there is a bug in the registration panel, so you can access the schedule and videos in this page.

Fresh picks from ArXiv
Today at ArXiv: approximate descent for training GNNs, expressive power of GNN, and GNN-doctor 💉

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

GNN
- Learning Graph Neural Networks with Approximate Gradient Descent AAAI 2021
- Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition ECCV 20
- Learning Node Representations from Noisy Graph Structures ICDM 2020
- Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs Workshop NeurIPS 2020
- Graph Neural Networks for Improved El Niño Forecasting Workshop NeurIPS 2020

Applications
- Bayesian Graph Neural Networks for Molecular Property Prediction
- Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing
- Partial Gromov-Wasserstein Learning for Partial Graph Matching
- Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results with Stefanie Jegelka
- Graph Mixture Density Networks


Survey
- A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources

ML News

New website that covers ML news, a clone of hacker news but dedicated to ML. In addition to Lobste.rs and Slashdot.

MoleculeKit: Machine Learning Methods for Molecular Property Prediction and Drug Discovery

MoleculeKit is a new framework that deals with molecule predictions. It represents molecules as both graphs and sequences and then apply GNN or kernel together with BERT for downstream molecular tasks (predicting properties of nodes or graphs).

Privacy-Preserving Deep Learning Over Graphs

60 slides of overview of the emerging field of privacy-preserving GNNs. Could be interesting if you search for a new research topic.

Graph Machine Learning research groups: Max Welling

I do a series of posts on the groups in graph research, previous post is here. The 20th is Max Welling, the head of the Amsterdam Machine Learning Lab. He co-founded a startup Scyfer BV that was acquired by Qualcomm, where he serves as VP of technologies. Max has a diverse research interests, including lately developments in graph machine learning field.

Max Welling (1968)
- Affiliation: University of Amsterdam, Qualcomm
- Education: Ph.D. at Utrecht University in 1998 (advisor: Gerard 't Hooft)
- h-index 73
- Awards: ECCV Koenderink Prize, ICML best papers.
- Interests: equivariant networks, variational encoders, GNNs.

Deep Graph Networks Reading Group

There is a reading group at Bicocca University (Milan, Italy). Next session will happen on Monday, 14th December at 10am (UK time). The paper "HATS a hierarchical graph attention network for stock movement prediction" will be discussed. If you want to join you can get a link by contacting @Sagax_ita or via [email protected].

Machine Learning on Knowledge Graphs @ NeurIPS 2020

A timely digest of NeurIPS 2020 by Michael Galkin. He speaks on improvement over Query2Box, how NAS and meta-learning works in KG domain, constructing the queries from the natural language, and several KG datasets. Worth a read!

GML Newsletter - Issue #5: Was 2020 a good year for graph research?

My new newsletter is out! 🔥 Talking about my predictions for 2020, NeurIPS recordings, ICLR submissions and a few links that you probably have seen already, my friends!

Fresh picks from ArXiv
Today at ArXiv: application of GNNs to drug discovery, graph construction by Wallmart, and improving expressiveness via more injective functions 😎

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

GNN
- Breaking the Expressive Bottlenecks of Graph Neural Networks
- Building Graphs at a Large Scale: Union Find Shuffle
- Utilising Graph Machine Learning within Drug Discovery and Development with Michael Bronstein
- Molecular graph generation with Graph Neural Networks

Conferences
- GDPNet: Refining Latent Multi-View Graph for Relation Extraction AAAI 2021
- Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation AAAI 2021
- Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation AAAI 2021
- Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation AAAI 2021
- Context-Aware Graph Convolution Network for Target Re-identification AAAI 2021
- Overcoming Catastrophic Forgetting in Graph Neural Networks AAAI 2021
- Bipartite Graph Embedding via Mutual Information Maximization WSDM 2021
- A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings Workshop NeurIPS 2021
- Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction Workshop NeurIPS 2020

Survey
- Deep Analysis on Subgraph Isomorphism
- The Future is Big Graphs! A Community View on Graph Processing Systems
- A Note on Spectral Graph Neural Network

How Knowledge Graphs Will Transform Data Management And Business

Nice article that describes how different companies including BenevolentAI are using knowledge graphs and what are the challenges of using them.

Generalization Bounds of GNN

Expressiveness, that is what class of graphs can be represented by GNN, has been extensively studied during the last two years. On the other hand, generalization, i.e. ability to represent correctly unseen graphs is just gaining attention. Here are some papers that study generalization of GNN.

- Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks NeurIPS 2020
- Generalization and Representational Limits of Graph Neural Networks ICML 2020
- Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case ICML 2020
- A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks Arxiv Dec 2020

Machine Learning for Graphs and Sequential Data (MLGS)

Awesome course by Stephan Günnemann covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more ⭐

Fresh picks from ArXiv
Today at ArXiv: new transformers for graphs, rethinking spectral GNNs, and capsule graph nets 💊

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

Conferences
Hierarchical Graph Capsule Network AAAI 2021
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training AAAI 2021
Enhancing Balanced Graph Edge Partition with Effective Local Search AAAI 2021
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting AAAI 2021
An Experimental Study of the Transferability of Spectral Graph Networks Workshop AAAI 2021, with Xavier Bresson
A Generalization of Transformer Networks to Graphs Workshop AAAI 2021, with Xavier Bresson
Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks Workshop AAAI 2021

Applications
Deep Reinforcement Learning of Graph Matching
A pipeline for fair comparison of graph neural networks in node classification tasks
A Note on Graph-Based Nearest Neighbor Search

Survey
Graph Neural Networks: Taxonomy, Advances and Trends

GNN Paper Explained

Looks like a promising YouTube series on graph machine learning, with the first video explaining the GAT paper.

Fresh picks from ArXiv
Today at ArXiv: random fields for graphs, deconvolutional networks, and general routing algorithms 🛣

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

Conferences
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks AAAI 2021
Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification AAAI 2021
Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos AAAI 2021
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation AAAI 2021


Applications
On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights with Leman Akoglu
Motif-Driven Contrastive Learning of Graph Representations
Deep Multi-attribute Graph Representation Learning on Protein Structures
Graph Autoencoders with Deconvolutional Networks
A Generalized A* Algorithm for Finding Globally Optimal Paths in Weighted Colored Graphs

Survey
Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

Tsinghua University Releases First AutoML Toolkit for Graph Datasets & Tasks

At least in the research papers, hyperparameter tuning, feature engineering, architecture and model search, stacking and boosting have been largely ignored and I have a good faith that in the coming year there will be more papers that extensively perform all of those to gain additional boost in performance. This AutoGL PyTorch Framework does just this. It's based off PyG, contains a few standard datasets and models, has several HP algorithms, generates graphlet, pagerank, and other features, stacks models' predictions, and more. Looks very promising.

Paper Explained: Principal Neighbourhood Aggregation for Graph Nets

A nice explanation by Andrei Margeloiu about NeurIPS 2020 paper on how to "fix" GNN's expressivity for continuous node features. More video explanations of GML papers!