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cs.LG2025
Gumbel-MPNN: Graph Rewiring with Gumbel-Softmax
Marcel Hoffmann, Lukas Galke, Ansgar Scherp
Graph homophily has been considered an essential property for message-passing neural networks (MPNN) in node classification. Recent findings suggest that performance is more closel…
cs.LG2025
Active Few-Shot Learning for Vertex Classification Starting from an Unlabeled Dataset
Felix Burr, Marcel Hoffmann, Ansgar Scherp
Despite the ample availability of graph data, obtaining vertex labels is a tedious and expensive task. Therefore, it is desirable to learn from a few labeled vertices only. Existin…
cs.LG2024
Edge-Splitting MLP: Node Classification on Homophilic and Heterophilic Graphs without Message Passing
Matthias Kohn, Marcel Hoffmann, Ansgar Scherp
Message Passing Neural Networks (MPNNs) have demonstrated remarkable success in node classification on homophilic graphs. It has been shown that they do not solely rely on homophil…