5 papers
A Unifying Relational Perspective on Expressive Lottery Tickets
Lorenz Kummer, Samir Moustafa, Anatol Ehrlich +4
Graph neural networks (GNNs) are widely used, but how parameter sparsity affects the expressivity of relational (RGNNs) and temporal (TGNNs) variants is poorly understood. The Stro…
Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?
Franka Bause, Jonas Niederle, Martin Pawelczyk +1
The effectiveness of multi-agent LLM deliberation depends not only on the agents' individual predictions, but also on how they communicate and collaborate. We study this mechanism…
XIMP: Cross Graph Inter-Message Passing for Molecular Property Prediction
Anatol Ehrlich, Lorenz Kummer, Vojtech Voracek +2
Accurate molecular property prediction is central to drug discovery, yet graph neural networks often underperform in data-scarce regimes and fail to surpass traditional fingerprint…
Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win
Lorenz Kummer, Samir Moustafa, Anatol Ehrlich +4
The lottery ticket hypothesis (LTH) is well-studied for convolutional neural networks but has been validated only empirically for graph neural networks (GNNs), for which theoretica…
Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph
Andreas Roth, Franka Bause, Nils M. Kriege +1
The ability of message-passing neural networks (MPNNs) to fit complex functions over graphs is limited as most graph convolutions amplify the same signal across all feature channel…