On Varying Topology of Complex Networks and Performance Limitations of Community Detection Algorithms

TL;DR

This paper evaluates how different community detection algorithms perform across networks with varying topological features, revealing limitations and emphasizing the need for algorithms that adapt to structural variations.

Contribution

It introduces a new Naive Scale Free Model for testing community detection algorithms against diverse topologies, highlighting their performance limitations.

Findings

Current algorithms often fail to accurately detect communities in networks with varying topologies.

Performance varies significantly with network size, community size, and connectivity.

Existing algorithms need redesign to better account for topological diversity.

Abstract

One of the most widely studied problem in mining and analysis of complex networks is the detection of community structures. The problem has been extensively studied by researchers due to its high utility and numerous applications in various domains. Many algorithmic solutions have been proposed for the community detection problem but the quest to find the best algorithm is still on. More often than not, researchers focus on developing fast and accurate algorithms that can be generically applied to networks from a variety of domains without taking into consideration the structural and topological variations in these networks. In this paper, we evaluate the performance of different clustering algorithms as a function of varying network topology. Along with the well known LFR model to generate benchmark networks with communities,we also propose a new model named Naive Scale Free Model to study the behavior of community detection algorithms with respect to different topological features. More specifically, we are interested in the size of networks, the size of community structures, the average connectivity of nodes and the ratio of inter-intra cluster edges. Results reveal several limitations of the current popular network clustering algorithms failing to correctly find communities. This suggests the need to revisit the design of current clustering algorithms that fail to incorporate varying topological features of different networks.