Improved Approximation Algorithms for Bipartite Correlation Clustering

In this work we study the problem of Bipartite Correlation Clustering (BCC), a natural bipartite counterpart of the well studied Correlation Clustering (CC) problem. Given a bipartite graph, the objective of BCC is to generate a set of vertex-disjoint bi-cliques (clusters) which minimizes the symmetric difference to it. The best known approximation algorithm for BCC due to Amit (2004) guarantees an 11-approximation ratio.4 In this paper we present two algorithms. The first is an improved 4approximation algorithm. However, like the previous approximation algorithm, it requires solving a large convex problem which becomes prohibitive even for modestly sized tasks. The second algorithm, and our main contribution, is a simple randomized combinatorial algorithm. It also achieves an expected 4-approximation factor, it is trivial to implement and highly scalable. The analysis extends a method developed by Ailon, Charikar and Newman in 2008, where a randomized pivoting algorithm was analyzed...

• Type: Journal
• Year: 2011
• Where: ESA
• Authors: Nir Ailon, Noa Avigdor-Elgrabli, Edo Liberty, Anke van Zuylen
• Keywords: Algorithms, Approximation Algorithm, Approximation Factor, Deterministic Algorithm, ESA 2011