AI Research Scientist @Intel AI Lab. Prev: Postdoc @Mila_Quebec & McGill. GraphML, Knowledge Graphs, GNNs, NLP.
Grandmaster of 80's music (according to Spotify)
Jan 16 • 9 tweets • 2 min read
📣Two new blog posts - a comprehensive review of Graph and Geometric ML in 2023 with predictions for 2024.
Together with @mmbronstein, we asked 30 academic and industrial experts about the most important things happened in their areas and open challenges to be solved.
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Part I:
Theory of GNNs, New and Exotic Message Passing, Going Beyong Graphs (with Topology, Geometric Algebras, and PDEs), Robustness, Graph Transformers, new datasets, community events, and top memes of 2023 (that’s what you are here for, right).towardsdatascience.com/graph-geometri…
Sep 15, 2020 • 7 tweets • 5 min read
We are glad to announce that our paper "Message Passing for Hyper-Relational Knowledge Graphs" by @michael_galkin@shape_mismatch@__gauravm@Ricardo_Usbeck@JLehmann82 has been accepted at @emnlp2020! 🎆 We propose a #GNN architecture for hyper-relational KGs like @wikidata. ⬇️
Traditional KGs are based on triples, whereas new KGs like #wikidata use statements and qualifiers to instantiate each edge further making the graph hyper-relational (img1). We incorporate these qualifiers by modifying existing multi-relational GNN (CompGCN) in the StarE (img 2).
