A scalable clustering algorithm to approximate graph cuts

TL;DR

This paper introduces Xist, a scalable clustering algorithm that approximates graph cuts efficiently by leveraging $st$-MinCut partitions, Gomory-Hu trees, and vertex selection, outperforming spectral clustering in large datasets.

Contribution

It presents a novel, efficient algorithm combining $st$-MinCut and Gomory-Hu trees for graph clustering, achieving linear runtime and better approximation quality.

Findings

Xist achieves linear runtime in vertices and quadratic in edges.

It outperforms spectral clustering in approximating graph cut values.

Successfully applied to biological image segmentation.

Abstract

Due to their computational complexity, graph cuts for cluster detection and identification are used mostly in the form of convex relaxations. We propose to utilize the original graph cuts such as Ratio, Normalized or Cheeger Cut to detect clusters in weighted undirected graphs by restricting the graph cut minimization to $st$-MinCut partitions. Incorporating a vertex selection technique and restricting optimization to tightly connected clusters, we combine the efficient computability of $st$-MinCuts and the intrinsic properties of Gomory-Hu trees with the cut quality of the original graph cuts, leading to linear runtime in the number of vertices and quadratic in the number of edges. Already in simple scenarios, the resulting algorithm Xist is able to approximate graph cut values better empirically than spectral clustering or comparable algorithms, even for large network datasets. We showcase its applicability by segmenting images from cell biology and provide empirical studies of runtime and classification rate.