MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks
Keywords: Graph neural networks, graph pooling, graph coarsening, maxcut
TL;DR: A GNN-based approach for computing MAXCUT on attributed graphs, used to implement graph pooling.
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 works well on any kind of 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.
Primary Area: learning on graphs and other geometries & topologies
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 337
Loading