Graph Machine Learning

Postdoc position at EPFL It's very interesting postdoc position at EPFL to work on molecule design. The following text is by Andreas Loukas. We are hiring a postdoc to work on the interface between AI and computational protein design. The project will be carried out at EPFL in collaboration with Bruno Correia, Michael Bronstein, Pierre Vandergheynst, and the Swiss Data Science Center. We offer a 2-year position in EPFL, a vibrant university (well.. post covid) located in one of the most beautiful countries. The salary is very competitive. The researcher will partake in an interdisciplinary effort to design novel proteins using tools from deep learning. The ideal candidate combines i) practical deep learning/GNN know-how ii) experience with generative models and/or reinforcement learning. Knowledge of biology is not required--but a willingness to learn is. Relevant work: https://tinyurl.com/1stzxmkj If you are interested, send me by email: a motivation letter explaining how your expertise fits the current position, a CV, the names/addresses of three references, and three selected publications. We will start reviewing applications on the 15th of March. Andreas Loukas (find email at andreasloukas.blog)

Graph workshop at AAAI 2021: videos Videos for a recent graph workshop at AAAI 2021 are available online. There are several keynotes including William Hamilton and Stephen Bach, as well as 2-min flash paper presentations.

Graph Neural Networks for Binding Affinity Prediction In-depth blog post about applications of GNN to drug discovery, and, in particular, to virtual screening for candidate molecules.

Recent applications of expanders to graph algorithms Informally, a graph is expander if the nodes are robustly connected, i.e. removing some edges would not break the connectivity. It has been used a lot to improve the running time of many graph algorithms. In this talk, there is a gentle introduction to expanders and their applications to static, dynamic, iterative, and distributed algorithms on graphs.

Fresh picks from ArXiv This week on ArXiv: connection between heterohpily and oversmoothing, SOTA unsupervised model, and MCTS for explainability 📞 GNNs * Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks with Danai Koutra * Bootstrapped Representation Learning on Graphs with Petar Veličković * On Explainability of Graph Neural Networks via Subgraph Explorations * Spherical Message Passing for 3D Graph Networks * A Unified Lottery Ticket Hypothesis for Graph Neural Networks * Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization * SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks Conferences * Learning Intents behind Interactions with Knowledge Graph for Recommendation WWW 2021 * Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks WWW 2021 * Model-Agnostic Graph Regularization for Few-Shot Learning with Jure Leskovec

Learning mesh-based simulation with Graph Networks Another work by DeepMind (ICLR '21) on how to simulate physical systems with GNN. The principle is the same as in their previous works: get a graph for a system, process it with GNN, obtain acceleration for each node, and provide it to Euler integrator to obtain positions of each node in the next step. Again, very cool visualizations.

Graph Machine Learning research groups: AustinR. Benson I do a series of posts on the groups in graph research, previous post is here. The 23rd is Austin R. Benson, a professor at Cornell, who together with his students recently shook the graph community by showing that label propagation works really well compared to GNN. Austin R. Benson (~1990) - Affiliation: Cornell - Education: Ph.D. at Stanford in 2017 (advisors: Jure Leskovec) - h-index 21 - Awards: best research papers at KDD, ASONAM, Kavli Fellow - Interests: label propagation, clustering, network algorithms

Job Posting for Research Scientist at NEC LabsEurope Several researcher positions are available at NEC Lab Europe, a research institute with a focus on CS/ML applications in life sciences. One includes working with Dr. Mathias Niepert who has been publishing many works in graph ML field. Deadline is 31st March.

Graph Neural Networks from the First Principles Petar Veličković will give a talk on 17 Feb about how GNNs appeared in different disciplines and how you can derive GNNs from permutation invariance. Petar has long worked in this field, knowing inside and out graph nets, so I strongly recommend to visit his talk. The link is here.

Graphs and More Complex Structures for Learning and Reasoning Workshop A workshop at AAAI 2021 featuring the talk about learning knowledge graph representations for zero-shot learning in NLP and vision.

How to get started with Graph Machine Learning In a new post, Aleksa Gordić talks in depth about graph ML, its applications and shares useful resources to get you started in this world.

Fresh picks from ArXiv This week on ArXiv: link prediction in KGs, unsupervised embedding library, and reconstruction conjecture for up to 13 vertices 💡 Conferences * Exploring the Subgraph Density-Size Trade-off via the Lovász Extension WSDM 2021 * Effective and Scalable Clustering on Massive Attributed Graphs WebConf 2021 GNN * Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations * CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Applications * Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs with William L. Hamilton * GNN-RL Compression: Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning Software * Cleora: A Simple, Strong and Scalable Graph Embedding Scheme Math * Reconstruction of small graphs and tournaments