Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track
Qing Xiu, Kai Han, Jing Tang, Shuang Cui, He Huang
Chromatic Correlation Clustering (CCC) (introduced by Bonchi et al. [6]) is a natural generalization of the celebrated Correlation Clustering (CC) problem, introduced by Bonchi et al. [6]. It models objects with categorical pairwise relationships by an edge-colored graph, and has many applications in data mining, social networks and bioinformatics. We show that there exists a $2.5$-approximation to the CCC problem based on a Linear Programming (LP) approach, thus improving the best-known approximation ratio of 3 achieved by Klodt et al. [21] . We also present an efficient heuristic algorithm for CCC leveraging a greedy clustering strategy, and conduct extensive experiments to demonstrate the effectiveness and efficiency of our proposed algorithm.