A Study on Modularity Density Maximization: Column Generation Acceleration and Computational Complexity Analysis

TL;DR

This paper enhances algorithms for maximizing modularity density in community detection by accelerating column generation, analyzing computational complexity, and demonstrating NP-hardness of related problems, with empirical validation on real-world networks.

Contribution

It introduces an accelerated column generation approach using dense subgraph discovery and reformulates the auxiliary problem as 0-1 linear programs, providing efficiency and diversity in solutions.

Findings

Accelerated column generation improves modularity density maximization.

Reformulation as 0-1 linear programming enhances computational efficiency.

NP-hardness of related variants is established.

Abstract

Community detection is a fundamental network-analysis primitive with a variety of applications in diverse domains. Although the modularity introduced by Newman and Girvan (2004) has widely been used as a quality function for community detection, it has some drawbacks. The modularity density introduced by Li et al. (2008) is known to be an effective alternative to the modularity, which mitigates one of the drawbacks called the resolution limit. A large body of work has been devoted to designing exact and heuristic methods for modularity density maximization, without any computational complexity analysis. In this study, we investigate modularity density maximization from both algorithmic and computational complexity aspects. Specifically, we first accelerate column generation for the modularity density maximization problem. To this end, we point out that the auxiliary problem appearing in column generation can be viewed as a dense subgraph discovery problem. Then we employ a well-known strategy for dense subgraph discovery, called the greedy peeling, for approximately solving the auxiliary problem. Moreover, we reformulate the auxiliary problem to a sequence of $0$--$1$ linear programming problems, enabling us to compute its optimal value more efficiently and to get more diverse columns. Computational experiments using a variety of real-world networks demonstrate the effectiveness of our proposed algorithm. Finally, we show the NP-hardness of a slight variant of the modularity density maximization problem, where the output partition has to have two or more clusters, as well as showing the NP-hardness of the auxiliary problem.