Graph Machine Learning

Manually-curated List of Combinatorial Conferences Mostly mathematical, with some occasions on CS, here is a manually-curated list of upcoming conferences, workshops, symposiums on combinatorics, among which you can find graph-related topics.

Machine Learning Summer School 2020 Today starts Machine Learning Summer School 2020 with great list of speakers on various topics of machine learning, including geometric deep learning. The lectures will be live-streamed on YouTube and are open to everyone.

Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework This is a post by Michael Galkin (@gimmeblues) about their new work on comprehensive evaluation of knowledge graph embeddings. A lot of interesting insights about knowledge graphs. Today we are publishing the results of our large-scale benchmarking study of knowledge graph (KG) embedding approaches. Further, we are releasing the code of PyKEEN 1.0 - the library behind the study (in PyTorch)! What makes KGs special: they often have hundreds or thousands of different relations (edge types), and having good representations is essential for reasoning in embedding spaces as well as for numerous NLP tasks. We often evaluate KG embeddings on the link prediction task - given subject+predicate, the model has to predict most plausible objects. As typical KGs contain 50k-100k different entities, you can guess the top1/top10 ranking task is quite complex! Why benchmarking is important: currently, there is no baseline numbers to refer to. Lots of papers in the domain are not reproducible, or the authors simply take metrics values as reported in other papers withougt reproducing their results. In this study, we ran 65K+ experiments and spent 21K+ GPU hours evaluating 19 models spanning from RESCAL first published in 2011 to the late 2019's RotatE and TuckER, 5 loss functions, training strategies with/without negative sampling, and many more hyper-parameters that turn out to be important to consider. Key findings: - Careful HPO optimization brings us new SOTA results giving significant gains of 4-5% compared to reported results in respective papers (btw, we used Optuna for HPO); - Properly tuned classical models (TransE, DistMult) are still good and actually outperform several newer models; - No Best-of-the-Best Silver Bullet model that beats all others across all tasks - some models better capture transitivity, whereas other better capture symmetric relations; - Surprisingly, for the inherently ranking task, the ranking loss (or MarginRankingLoss in PyTorch) is suboptimal. Instead, Cross-Entropy and its variations show better result; - Using all enities for negative sampling, i.e., sigmoid/softmax distribution over all enities, works well but can be quite expensive on large KGs. Stochastic negative sampling is a way to go then; - Computationally expensive and bigger models do not yield that big and drastic performance gains. In fact, 64-d Rotate is better than most 500-d models. Paper: https://arxiv.org/abs/2006.13365 Code: https://github.com/pykeen/pykeen

Sylow theorems and algebraic geometry There is a fresh thread on Sylow theorems, which are popular results in group theory. I'm not sure how much the waste of time is studying group theory, that's something in my todo list, but this thread is giving a good intro to it.

Top number of submissions at NeurIPS 2020 Mastodons of ML are the following: * Peter Richtárik (KAUST) 14 * Bernhard Schölkopf (MPI) 13 * Sergey Levine (UC Berkeley) 12 * Masashi Sugiyama (RIKEN) 11 * Yoshua Bengio (MILA) 11 This is based on 2313 arXiv papers that are submitted to NeurIPS2020. Last year there were at least some people with 15 submissions, so it's probably underestimates these numbers. Also, compared to last year there was 54% of the papers appearing in arXiv at the moment of the conference. For this year, today there are 25% of arXiv papers, so it means not everyone submitted their papers to arXiv.

Criteo papers at ICML 2020 Criteo, where I work, this year has record number of accepted papers at ICML. We have 9 papers on various topics, from online learning to theory of optimization to GANs. It makes us 1st company in EU and top-7 company worldwide (among 134 companies who have their papers accepted). So I wrote a short description of each paper in a new blog post.

Spektral Spektral is a library to code GNN in Tensorflow 2 and Keras. New version includes: - a unified message-passing interface based on gather-scatter - 7 new GNN layers - Huge performance improvements - Improved utils, docs, and examples The paper will be presented in GRL workshop.

Fresh picks from ArXiv This week highlights applications of GNNs to molecules, contagion, NLP, recommender systems and more. GNN • Generalizing Graph Neural Networks Beyond Homophily • Finding Patient Zero: Learning Contagion Source with Graph Neural Networks with Albert-László Barabási • MoFlow: An Invertible Flow Model for Generating Molecular Graphs • Quantifying Challenges in the Application of Graph Representation Learning • Neural Architecture Optimization with Graph VAE • Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning • Subgraph Neural Networks with Marinka Zitnik • Temporal Graph Networks for Deep Learning on Dynamic Graphs with Michael Bronstein • Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs with Andreas Loukas • Walk Message Passing Neural Networks and Second-Order Graph Neural Networks • Isometric Graph Neural Networks • Modeling Graph Structure via Relative Position for Better Text Generation from Knowledge Graphs • Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting with Michael Bronstein Math: • Local limit theorems for subgraph counts • Longest and shortest cycles in random planar graphs Conferences • How to Count Triangles, without Seeing the Whole Graph KDD 2020 • GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training KDD 2020 Surveys • Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison

Implicit Neural Representations by Yaron Lipman The talk Implicit Neural Representations by Yaron Lipman from CVPR 20 workshop on Deep Learning Foundations of Geometric Shape.