NeuroCUT: A Neural Approach for Robust Graph Partitioning

arXiv:2310.11787v2 Announce Type: replace
Abstract: Graph partitioning aims to divide a graph into disjoint subsets while optimizing a specific partitioning objective. The majority of formulations related to graph partitioning exhibit NP-hardness due to their combinatorial nature. Conventional methods, like approximation algorithms or heuristics, are designed for distinct partitioning objectives and fail to achieve generalization across other important partitioning objectives. Recently machine learning-based methods have been developed that learn directly from data. Further, these methods have a distinct advantage of utilizing …

abstract algorithms approximation arxiv cs.lg graph heuristics nature partitioning robust type