Graph Machine Learning

PhD Thesis on Graph Machine Learning Here are some PhD dissertations on GML. Part 3 (previous here). Xiaowen Dong: Multi-view signal processing and learning on graphs (EPFL 2014) Yan Leng: Collective behavior over social networks with data-driven and machine learning models (MIT 2020) Davide Boscaini: Geometric Deep Learning for Shape Analysis (Università della Svizzera Italiana 2017)

Fresh picks from ArXiv This week on ArXiV is an application of GNN to COVID forecasting, anew graph to sequence algo for machine translation, and a scikit library for network analytics ✍️ GNN - Recurrent Graph Tensor Networks - Image Retrieval for Structure-from-Motion via Graph Convolutional Network - United We Stand: Transfer Graph Neural Networks for Pandemic Forecasting KG - Inductive Learning on Commonsense Knowledge Graph Completion with Jure Leskovec - Type-augmented Relation Prediction in Knowledge Graphs NLP - Question Directed Graph Attention Network for Numerical Reasoning over Text EMNLP 20 - Graph-to-Sequence Neural Machine Translation Software - Scikit-network: Graph Analysis in Python

17th Workshop on Algorithms and Models for the Web Graph There is a pretty interesting workshop on graph theory and its application web graph. There are 5 talks each day, from 21 (today) to 24 Sept. The conference will be held online.

GNN course at UPenn In addition to cs224w at Stanford and COMP 766 at McGill (both should happen next semester), there is a good-looking currently ongoing course on Graph Neural Networks at University of Pennsylvania by Alejandro Ribeiro, who worked on graph ML and graph signal processing. This is a third week and there are already videos and assignments about graph convolutional filters, empirical risk minimization, and introduction to the field.

Graph ML at Data Fest 2020 Day 2 continued to surprise me as many people have joined on Sunday to listen to our talks. Especially interesting it was to see English-speaking participants who were not humble to ask questions and be present among so many Russian speakers. I see this English activity as a promising step in making ODS community truly global. Here is the second portion of videos, more related to applications of graphs. 1. Large Graph Visualization Tools and Approaches Sviatoslav Kovalev, Samokat, Russia 2. Business Transformation as Graph Problems Vadim Safronov, Key Points, Portugal 3. AutoGraph: Graphs Meet AutoML Denis Vorotinsev, Oura, Finland 4. Scene Graph Generation from Images Boris Knyazev, University of Guelph & Vector Institute, Canada 5. Link Prediction with Graph Neural Networks Maxim Panov, Skoltech, Russia My gratitude to all the speakers! Until next time!

Graph ML at Data Fest 2020 Day 1 was a pleasant surprise: people with different background came, watched videos, and asked questions. Here are 5 videos of day 1: 1. Opening remarks: Graph Machine Learning, Sergey Ivanov, Criteo, France (where I broadly talk about what is GML, what are the best resources, what's the community, etc.); 2. Graph-Based Nearest Neighbor Search: Practice and Theory, Liudmila Prokhorenkova, Yandex, Russia (where she spoke about her k-NN on graphs, HNSW, theory and her ICML 20 work); 3. Graphical Models for Tensor Networks and Machine Learning, Roman Schutski, Skoltech, Russia (where he spoke about graphical models, treewidth, tensor decomposition); 4. Unsupervised Graph Representations, Anton Tsistulin, University of Bonn & Google, Germany (where he spoke about all popular node embeddings methods and what their pros and cons); 5. Placing Knowledge Graphs in Graph ML, Michael Galkin, TU Dresden, Germany (it's all you need to know about knowledge graphs if you don't know what they are). On day 2, tomorrow, we will have 5 more videos, which would be about applications of graphs. Please, join us tomorrow at https://spatial.chat/s/ods at 12pm (Moscow time).

On Cora dataset Cora, Citeseer, and Pubmed are three popular data sets for node classification. It's one of those cases where you can clearly see the power of GNN. For example, on Cora GNNs have around 80% accuracy, while GBDT/MLP have only around 60%. This is not often the case: for many data sets I can see marginal win for GNN compared to non-graph methods and for some data sets it's actually lower. So why the performance of GNN is so great on this data set? I don't have a good answer for this, but here are some thoughts. Cora is a citation network, where nodes are papers and classes are papers' field. However, it's not clear what are the links between this documents. The original paper didn't describe how exactly links are established. If links were based on citation, i.e. two papers are connected if they have a citation from one to another, then it could explain such big improvement of GNN: GNN explore all nodes during training, while MLP only training nodes and since two papers likely to share the same field, GNN leverage this graph information. If that's the case simple k-nn majority vote baseline would be performing similar to GNN. However, there is an opinion from people who know the authors of the original paper saying that the links are established based on word similarity between documents. If that's true, I'm not sure why GNN is doing so well for this data set. In all cases, establishing the graphs from real-world data is something that requires a lot of attention and visibility, that's why structure learning is such an active topic.

Graph ML at Data Fest 2020 This year, together with @IggiSv9t, I organize a track at Data Fest 2020. It's like a workshop at the conference, but more informal. We will have videos from our amazing speakers and also networking, where you can speak to me, @IggiSv9t, speakers, or other people who are interested in graph machine learning. Besides our track there will be many other interesting tracks on all aspects of ML and DS (interpretability, antifraud, ML in healthcare, and 40 more tracks!). It will be this weekend, 19-20 September. You need to be registered (for free) at https://fest.ai/2020/. Our videos: Day 1 (Saturday) 1. Opening remarks: Graph Machine Learning, Sergey Ivanov, Criteo, France 2. Graph-Based Nearest Neighbor Search: Practice and Theory, Liudmila Prokhorenkova, Yandex, Russia 3. Graphical Models for Tensor Networks and Machine Learning, Roman Schutski, Skoltech, Russia 4. Unsupervised Graph Representations, Anton Tsistulin, University of Bonn & Google, Germany 5. Placing Knowledge Graphs in Graph ML, Michael Galkin, TU Dresden, Germany Day 2 (Sunday) 1. Large Graph Visualization Tools and Approaches, Sviatoslav Kovalev, Samokat, Russia 2. Business Transformation as Graph Problems, Vadim Safronov, Key Points, Portugal 3. Scene Graph Generation from Images, Boris Knyazev, University of Guelph & Vector Institute, Canada 4. AutoGraph: Graphs Meet AutoML, Denis Vorotinsev, Oura, Finland 5. Link Prediction with Graph Neural Networks, Maxim Panov, Skoltech, Russia See you there!

Fresh picks from ArXiv This week on ArXiv: robot planning in the presence of many objects by researchers at MIT, a new SOTA on probabilistic type inference for software, application of GNN to trying clothes to different body shapes 👚 Applications - Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks with Joshua Tenenbaum - Advanced Graph-Based Deep Learning for Probabilistic Type Inference - GINet: Graph Interaction Network for Scene Parsing ECCV 20 - Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On SIGGRAPH 2020 - Addressing Cold Start in Recommender Systems with Hierarchical Graph Neural Networks Theory - Learning an Interpretable Graph Structure in Multi-Task Learning - Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization - Adversarial Attack on Large Scale Graph

Latent graph neural networks: Manifold learning 2.0? One of the hot topics of this year is construction of a graph from unstructured data (e.g. 3d points or images). In a new post Michael Bronstein discusses existing approaches to latent graph learning and suggests that using GNN both to learn the structure of the graph and to solve the downstream tasks can be a better alternative than a de-coupled approach. This is indeed an exciting and active area of research with open problems and known applications to NLP, physics, and biology.

Graph Machine Learning research groups: DanaiKoutra I do a series of posts on the groups in graph research, previous post is here. The 14th is Danai Koutra, ex-PhD student of Christos Faloutsos, she leads the graph exploration lab at University of Michigan, and could be a great Ph.D. advisor if you are interested in GML. Danai Koutra (~1988) - Affiliation: University of Michigan - Education: Ph.D. in Carnegie Mellon University in 2010 (advisor: Christos Faloutsos) - h-index 25 - Awards: ACM SIGKDD 2016 Dissertation Award; best paper awards at ICDM, PAKDD, ICDT - Interests: graph mining, knowledge graphs, graph embeddings