Pascal Welke

@pascalwelke.bsky.social

Graph Machine Learning and Graph Mining Assistant Professor (Lecturer) in Data Science Lancaster University Leipzig https://pwelke.de

GLOW is returning on 𝗠𝗮𝗿𝗰𝗵 𝟮𝟲𝘁𝗵, 𝟱𝗽𝗺 𝗖𝗘𝗧 with a special guest: @petar-v.bsky.social 🌟 He will lecture on LLMs as GNNs – a topic which received quite some attention at our last session. Specifically, we will learn how Graph ML tools can help understand LLM generalisation

Today at NeurIPS: Weisfeiler and Leman go Loopy: A New Hierarchy for Graph Representational Learning Cycles are important for predictive tasks on chemical molecules. We allow message passing along neighboring paths. Our architecture can subgraph-count cycles and homomorphism-count cactus graphs.

Visual depiction of r-lGIN: During preprocessing, we calculate the path neighborhoods Nr (v) for each node v in the graph G. Paths of varying lengths are processed separately using simple GINs, and their embeddings are pooled to obtain the final graph embedding. The forward complexity scales linearly with the sizes of Nr (v), enabling efficient computation on sparse graphs.

Had an incredible time at the Learning on Graphs meetup in Paris! Amazing energy, awesome presentations, and lovely posters. I was honored to give a keynote on graph representation learning via homomorphisms and loved the insightful questions and vibrant discussions.