Complex network community discovery using fast local move iterated greedy algorithm

TL;DR

The paper introduces FLMIG, a fast and efficient algorithm for detecting community structures in complex networks, improving accuracy and speed over existing methods.

Contribution

It presents a novel community detection algorithm combining local heuristics and greedy strategies, enhancing modularity optimization in large-scale networks.

Findings

FLMIG outperforms existing algorithms in accuracy.

FLMIG demonstrates faster convergence on synthetic and real networks.

The method effectively handles large-scale and dynamic networks.

Abstract

Examining the community structures within intricate networks is crucial for comprehending their intrinsic dynamics and functionality. The paper presents the Fast Local Move Iterated Greedy (FLMIG) algorithm, a novel method designed to effectively identify community structures in intricate networks. The FLMIG algorithm improves the modularity optimization process by including a rapid local move heuristic and an iterated greedy mechanism that switches between destructive and constructive phases to strengthen the community partitions. The main innovation is the integration of random neighbor moves with an enhanced Prune Louvain algorithm, which guarantees fast convergence while maintaining the connection of the identified communities. The results of our comprehensive studies, conducted on both synthetic and and real-world networks, clearly show that FLMIG surpasses existing cutting-edge techniques in terms of both accuracy and computing efficiency. This algorithm not only provides a strong tool for identifying communities, but also makes a valuable contribution to the broader field of network analysis by offering a method that can effectively handle large-scale and dynamically evolving networks.