An Improved Algorithm for Bipartite Correlation Clustering

TL;DR

This paper introduces a simple combinatorial algorithm that improves the approximation factor for bipartite correlation clustering from 11 to 4 without solving large convex programs, making it more practical for larger problems.

Contribution

The paper presents a novel 4-approximation algorithm for bipartite correlation clustering that avoids large convex program solutions, extending analysis techniques from correlation clustering.

Findings

Achieved a 4-approximation factor, improving over the previous 11.

Developed a combinatorial algorithm that does not require solving large convex programs.

Extended analysis methods to consider subgraph structures of unbounded size.

Abstract

Bipartite Correlation clustering is the problem of generating a set of disjoint bi-cliques on a set of nodes while minimizing the symmetric difference to a bipartite input graph. The number or size of the output clusters is not constrained in any way. The best known approximation algorithm for this problem gives a factor of 11. This result and all previous ones involve solving large linear or semi-definite programs which become prohibitive even for modestly sized tasks. In this paper we present an improved factor 4 approximation algorithm to this problem using a simple combinatorial algorithm which does not require solving large convex programs. The analysis extends a method developed by Ailon, Charikar and Alantha in 2008, where a randomized pivoting algorithm was analyzed for obtaining a 3-approximation algorithm for Correlation Clustering, which is the same problem on graphs (not bipartite). The analysis for Correlation Clustering there required defining events for structures containing 3 vertices and using the probability of these events to produce a feasible solution to a dual of a certain natural LP bounding the optimal cost. It is tempting here to use sets of 4 vertices, which are the smallest structures for which contradictions arise for Bipartite Correlation Clustering. This simple idea, however, appears to be evasive. We show that, by modifying the LP, we can analyze algorithms which take into consideration subgraph structures of unbounded size. We believe our techniques are interesting in their own right, and may be used for other problems as well.