Graph Machine Learning

Deep Learning on Graphs: Method and Applications (DLG-AAAI’21) AAAI workshop on GNNs with a amazing list of speakers including Jure Leskovec, Max Welling, Xavier Bresson, and many others. Zoom link is available here, starting today 5pm Europe time.

Cleora Paper I already wrote about Cleora, an unsupervised embedding library, now there is a paper explaining details of it. The algorithm is just some form of matrix multiplication, yet it shows better performance for link prediction metrics and running time than Pytorch-BigGraph, DeepWalk and others.

Sberloga Talk In case you speak Russian I will be presenting today our ICLR 2021 work about combination of GBDT with GNN on graphs with tabular features. The talk will be 19-00 MSK time. Zoom link will be shared soon at @sberlogawithgraphs. For more videos from Sberloga, subscribe here: https://www.youtube.com/c/SBERLOGA

Tutorial: Graph Neural Networks: Models and Applications A new tutorial covering robustness, attacks, scalability and self-supervised learning for GNN models at AAAI 2021. Slides and video are available.

How many paths of length k exist in a graph? In case you are preparing for the next interview, here is a nice post describing several solutions to a common interview problem: count the number of possible walks between two points in a graph. The problem is not as easy as it seems.

Fresh picks from ArXiv This week on ArXiv: tensorflow GNN library, survey on graph-based kNN search, and automation of peer review? 🧐 Conferences Interpreting and Unifying Graph Neural Networks with An Optimization Framework WWW 2021 A Graph-based Relevance Matching Model for Ad-hoc Retrieval AAAI 2021 Software Efficient Graph Deep Learning in TensorFlow with tf_geometric Survey *A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search * Graph Neural Network for Traffic Forecasting: A Survey * Can We Automate Scientific Reviewing?

GML Newsletter: Interpolation and Extrapolation of Graph Neural Networks The new issue of the newsletter is about generalization of GNNs. Compared to the study of expressive power, there are fewer works about generalization. Nonetheless, I gathered the most exciting research I found on this topic, which I hope will familiarize you with this research direction.

Video: GNN User Group The video from the first meeting of GNN user group talks about the usage and next release of DGL and featuring Le Song with combinatorial optimization talk.

CS224W: Machine Learning with Graphs 2021 CS224W is one of the most popular graph courses by Jure Leskovec at Stanford. This year includes extra topics such as label propagation, scalability of GNNs, and graph nets for science and biology. The slides for the first 6 out of 20 lectures are available.

RoboGrammar: Graph Grammar for Terrain-Optimized Robot Design (video) A recent work done at MIT for constructing different robot designs via graph grammar. Graph grammars were introduced in 1992 and defines a set of rules of transforming one graph to another. With this, a user can specify input robot components as well as the type of the terrain and graph grammar will produce possible robot designs. Next, a variation of A* algorithm is used to search for the optimal robot design for a given terrain. More on this in this article.

GNN User Group events First event at GNN User Group organized by DGL team (Amazon) and CuGraph team (Nvidia) starts tomorrow. Events should be organized monthly. The first talk is "A Framework For Differentiable Discovery of Graph Algorithms (Dr. Le Song, Georgia Tech)" + some networking event.

Course: ODS Knowledge Graphs Michael Galkin starts a self-paced course on knowledge graphs. For now, it's only in Russian, with the plan to make it in English after the first iteration. The first introduction lecture is available on YouTube. You can join discussion group for all your questions and proposals: @kg_course. The first lecture starts this Thursday, more in the channel @kg_course. Course curriculum: * Knowledge representations (RDF, RDFS, OWL) * Storage and queries (SPARQL, Graph DBs) * Consistency (RDF*, SHACL, ShEx) * Semantic Data Integration * Graph theory intro * KG embeddings * GNNs for KGs * Applications: Question Answering, Query Embeddings