On the accurate computation of expected modularity in probabilistic networks

TL;DR

This paper introduces FPWP, an efficient method for accurately computing the expected modularity in probabilistic networks, outperforming existing approaches in speed while maintaining precision.

Contribution

The paper presents a novel and efficient technique (FPWP) for computing the probability distribution and expected value of modularity in probabilistic networks.

Findings

FPWP is faster than brute-force methods.

Removing low-probability edges yields inaccurate results.

Sampling methods' convergence varies with network parameters.

Abstract

Modularity is one of the most widely used measures for evaluating communities in networks. In probabilistic networks, where the existence of edges is uncertain and uncertainty is represented by probabilities, the expected value of modularity can be used instead. However, efficiently computing expected modularity is challenging. To address this challenge, we propose a novel and efficient technique (FPWP) for computing the probability distribution of modularity and its expected value. In this paper, we implement and compare our method and various general approaches for expected modularity computation in probabilistic networks. These include: (1) translating probabilistic networks into deterministic ones by removing low-probability edges or treating probabilities as weights, (2) using Monte Carlo sampling to approximate expected modularity, and (3) brute-force computation. We evaluate the accuracy and time efficiency of FPWP through comprehensive experiments on both real-world and synthetic networks with diverse characteristics. Our results demonstrate that removing low-probability edges or treating probabilities as weights produces inaccurate results, while the convergence of the sampling method varies with the parameters of the network. Brute-force computation, though accurate, is prohibitively slow. In contrast, our method is much faster than brute-force computation, but guarantees an accurate result.