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We have two papers accepted at #NeurIPS2022 on neural sheaf diffusion and subgraph GNNs Congratulations to amazing coauthors Francesco Di Giovanni Cristian Bodnar @b_p_chamberlain Pietro Lio' Fabrizio Frasca Haggai Maron Beatrice Bevilacqua

One of the best GNN papers this year, an anonymous ICLR submission: openreview.net/pdf?id=r9hNv76… Shows 4 families of efficient+expressive GNNs cannot compute biconnectivity properties (based on cut nodes/edges), but our ESAN framework (arxiv.org/abs/2110.02910) can compute them! 1/n

Our paper “Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries” was accepted as an 𝗼𝗿𝗮𝗹 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 at #NeurIPS2022! Check out our recently released code: github.com/beabevi/SUN arxiv.org/abs/2206.11140

Don't miss our latest work showing how invariances can once again help us in graph learning! Now we look at causal tasks!

💻🕸️Today at #ICML2023, Beatrice Bevilacqua will present her internship work Google DeepMind; I'll also be there! We design a causality objective capturing a simple idea: many inputs to a target algorithm will induce identical initial behaviours. 2--3:30pm; Poster #227. See you there! 😊

Do you want to know how to learn to select a 𝐬𝐦𝐚𝐥𝐥 𝐬𝐮𝐛𝐬𝐞𝐭 𝐨𝐟 𝐬𝐮𝐛𝐠𝐫𝐚𝐩𝐡𝐬 for Subgraph GNNs? Stop by our iclrconf 𝐏𝐨𝐬𝐭𝐞𝐫 #𝟖𝟓 𝐓𝐨𝐝𝐚𝐲 𝐚𝐭 𝟏𝟎:𝟒𝟓 to find out! w/ Bruno Ribeiro Haggai Maron - arxiv.org/abs/2310.20082 #ICLR2024

📣Stop by our NeurIPS Conference Poster #3103 Today at 11 AM! for DiGRAF: Diffeomorphic Graph Adaptive Activation Function - an activation function that adapts to the graph structure by learning a diffeomorphism. Joint Work with Xinzhi Wang (equal contrib.), Carola-Bibiane Schönlieb, Bruno Ribeiro ,