Online Clique Clustering

TL;DR

This paper studies online algorithms for clique clustering, proposing strategies with competitive ratios for maximizing intra-cluster edges and analyzing the difficulty of minimizing inter-cluster edges.

Contribution

It introduces an online strategy with bounded competitive ratios for maximizing intra-cluster edges and establishes lower bounds for deterministic strategies.

Findings

Asymptotic competitive ratio at most 15.646 for maximizing edges

Absolute competitive ratio at most 22.641 for maximizing edges

No deterministic strategy can have an asymptotic ratio better than 6

Abstract

Clique clustering is the problem of partitioning the vertices of a graph into disjoint clusters, where each cluster forms a clique in the graph, while optimizing some objective function. In online clustering, the input graph is given one vertex at a time, and any vertices that have previously been clustered together are not allowed to be separated. The goal is to maintain a clustering with an objective value close to the optimal solution. For the variant where we want to maximize the number of edges in the clusters, we propose an online strategy based on the doubling technique. It has an asymptotic competitive ratio at most 15.646 and an absolute competitive ratio at most 22.641. We also show that no deterministic strategy can have an asymptotic competitive ratio better than 6. For the variant where we want to minimize the number of edges between clusters, we show that the deterministic competitive ratio of the problem is $n-\omega(1)$, where n is the number of vertices in the graph.