Graph Machine Learning

TRIPODS Winter School & Workshop on Graph Learning and Deep Learning A series of tutorials and hands-on sessions, followed by a workshop covering recent results on graph ML by top researchers in this field. Starts today, requires registration (probably directly asking organizers).

Fresh picks from ArXiv Today at ArXiv: thesis on graph matching, image search with scene graphs, and decentralized agent's control 👮 If I forgot to mention your paper, please shoot me a message and I will update the post. Conferences * Image-to-Image Retrieval by Learning Similarity between Scene Graphs AAAI 21 * A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification Workshop AAAI 21 Applications * Decentralized Control with Graph Neural Networks with Alejandro Ribeiro * Graph Networks with Spectral Message Passing with Peter Battaglia Survey * Some Algorithms on Exact, Approximate and Error-Tolerant Graph Matching * Algorithms for Learning Graphs in Financial Markets

Book: Probabilistic Machine Learning: An Introduction In addition to two books dedicated to graph ML that I described in the past, there is a new draft of ML book that includes a chapter on graph embeddings. This describes graph embeddings as a encoder-decoder problem and dives into unsupervised and supervised ways to define encoder/decoder parts. It covers matrix factorization methods, label propagation, GNNs, and applications of embeddings.

Happy New Year 2021! Thank you all who followed and shared my posts this year! My very first post was a year ago and since then I wrote 380+ more. The community grew to 1700+ subscribers, who motivated me to learn more and share exciting works done in this community. In 2021 I wish you stay connected in this disconnected world! Peace.

Geometric ML becomes real in fundamental sciences A new post by Michael Bronstein: top-3 papers in 2020 about applications of graphML to drug development. I agree that this field is getting momentum and more companies, small and big, will look into application of GNNs to molecule predictions. There is even a graph ML researcher position available in the industrial company. Exciting times for those who are interested in graphs, ML, and biology.

Graph Machine Learning: Highlights 2020 Here is my short presentation at ODS (video in Russian) about the state of Graph ML in 2020: top-3 applications and perspectives for the next year.

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!

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.

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