15 citations · 35 across the 11 of their papers we have counts for
5 papers · 1 filter
Beyond Pairwise Correlations: Higher-Order Redundancies in Self-Supervised Representation Learning
David Zollikofer, Béni Egressy, Frederik Benzing +2
Several self-supervised learning (SSL) approaches have shown that redundancy reduction in the feature embedding space is an effective tool for representation learning. However, the…
Graph Dimension Attention Networks for Enterprise Credit Assessment
Shaopeng Wei, Beni Egressy, Xingyan Chen +4
Enterprise credit assessment is critical for evaluating financial risk, and Graph Neural Networks (GNNs), with their advanced capability to model inter-entity relationships, are a…
Graphtester: Exploring Theoretical Boundaries of GNNs on Graph Datasets
Eren Akbiyik, Florian Grötschla, Beni Egressy +1
Graph Neural Networks (GNNs) have emerged as a powerful tool for learning from graph-structured data. However, even state-of-the-art architectures have limitations on what structur…
Provably Powerful Graph Neural Networks for Directed Multigraphs
Béni Egressy, Luc von Niederhäusern, Jovan Blanusa +3
This paper analyses a set of simple adaptations that transform standard message-passing Graph Neural Networks (GNN) into provably powerful directed multigraph neural networks. The…
Graph Neural Networks with Precomputed Node Features
Beni Egressy, Roger Wattenhofer
Most Graph Neural Networks (GNNs) cannot distinguish some graphs or indeed some pairs of nodes within a graph. This makes it impossible to solve certain classification tasks. Howev…