Combinatorial optimization (CO) problems are notoriously challenging for neural networks,
especially in the absence of labeled instances. This work proposes an unsupervised …

We study the problem of graph clustering where the goal is to partition a graph into clusters,
ie disjoint subsets of vertices, such that each cluster is well connected internally while …

Hypergraphs are a useful abstraction for modeling multiway relationships in data, and
hypergraph clustering is the task of detecting groups of closely related nodes in such data …

The minimum st cut problem in graphs is one of the most fundamental problems in
combinatorial optimization, and graph cuts underlie algorithms throughout discrete …

Graph clustering has many important applications in computing, but due to growing sizes of
graphs, even traditionally fast clustering methods such as spectral partitioning can be …

Hypergraph-based machine learning methods are now widely recognized as important for
modeling and using higher-order and multiway relationships between data objects. Local …

The hardness of combinatorial optimization (CO) problems hinders collecting solutions for
supervised learning. However, learning neural networks for CO problems is notoriously …

Vertex connectivity a classic extensively-studied problem. Given an integer k, its goal is to
decide if an n-node m-edge graph can be disconnected by removing k vertices. Although a …

Local clustering aims at extracting a local structure inside a graph without the necessity of
knowing the entire graph structure. As the local structure is usually small in size compared to …

Flow-based methods for local graph clustering have received significant recent attention for
their theoretical cut improvement and runtime guarantees. In this work we present two …