Post #230

Main theme from GRL+ workshop I already mentioned it, but let me add more things about trends in GML (credits to Petar Veličković). The biggest theme from GRL+ workshop was the explicit consideration of structure, which so far was largely ignored in GNNs (i.e. one would just assume a given graph without thinking how it got there or whether it could be specialized for the task at hand). In the accepted papers, we have many works which tackle latent structure inference (e.g. Differentiable Graph Module, set2graph, Relate-and-Predict, GFSA, and our PGN are all examples thereof) and also works which attempt to explicitly exploit structure in the data for prediction (e.g. the recent subgraph isomorphism counting paper). This direction was echoed a lot in our invited talks as well. Thomas Kipf was talking about relational structure discovery (NRI, CompILE and his recent slot attention work). Kyle Cranmer was talking about how critical structure discovery is in physics-based applications and inductive biases, highlighting especially his set2graph work as well as their recent work on discovering symbolic representations. Danai Koutra talking how graphs can be appropriately summarized and how to design GNN layers to deal with heterophily. Tina Eliassi-Rad gave an amazing lecture-style talk on how topology and structure can be leveraged in machine learning more generally. During our Q&A session, she was asked to give comments on the explosive usage datasets like Cora (as she is one of the authors on the paper that originally proposed Cora, Citeseer etc). And she made a very important 'wakeup call' to GRL folks that we shouldn't think our graphs fall from the sky, and on the topic of using heavy-duty GNN methods and hyperbolic embeddings, etc in the real world, we should always ask the question: 'do we really expect our graphs to be coming from a distribution like this?'. The videos with all of it should be available in the coming weeks.