* in the afternoon session 😅

Happening today, East Exhibit Hall A-C, poster #3110. Come say "Hi!"! 👋

6/6 Interested in learning more? Check out our preprint here: arxiv.org/pdf/2405.17311.
If you’d like to discuss, I’d be very happy to chat during the poster session in Vancouver! :)

5/6 How it works: Probabilistic sampling connects original nodes to virtual ones, enhancing connectivity without explicit pairwise computations. The result is a framework that achieves both higher WL expressiveness and efficiency in graph-based learning.

4/6 We demonstrate SOTA results on various benchmarks, effectively addressing over-squashing and under-reaching. IPR-MPNNs also surpass standard MPNNs in expressiveness, distinguishing complex graph structures—all while being faster and more memory-efficient than GTs. 🚀

3/6 Enter IPR-MPNNs: Our approach learns to rewire graphs probabilistically by adding virtual nodes. This eliminates the need for heuristics, making the method more flexible and task-adaptive, while maintaining computational efficiency. 🎯

2/6 Standard MPNNs struggle with long-range interactions, making them less effective for large, complex graphs. Transformers help but come with quadratic complexity, which is computationally expensive. Rewiring heuristics? Often brittle and task-specific.

Genuine question - why do captions go above tables? I've always assumed that this is due to wanting to make tables distinct from figures, but it seems like a convention.

👀👋