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Computer Science > Machine Learning

arXiv:2409.05100 (cs)
[Submitted on 8 Sep 2024 (v1), last revised 10 Sep 2024 (this version, v2)]

Title:MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks

Authors:Carlo Abate, Filippo Maria Bianchi
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Abstract:We propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges. Our approach is robust to the underlying graph topology and is fully differentiable, making it possible to find solutions that jointly optimize the MAXCUT along with other objectives. Based on the obtained MAXCUT partition, we implement a hierarchical graph pooling layer for Graph Neural Networks, which is sparse, differentiable, and particularly suitable for downstream tasks on heterophilic graphs.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2409.05100 [cs.LG]
  (or arXiv:2409.05100v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2409.05100

Submission history

From: Filippo Maria Bianchi [view email]
[v1] Sun, 8 Sep 2024 14:06:25 UTC (3,186 KB)
[v2] Tue, 10 Sep 2024 08:00:19 UTC (3,186 KB)
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