Beyond Asymptotics: Practical Insights into Community Detection in Complex Networks
Tianjun Ke, Zhiyu Xu

TL;DR
This paper compares the practical performance of various community detection algorithms in complex networks, revealing their strengths and limitations under different conditions to guide real-world application choices.
Contribution
It provides a comprehensive empirical evaluation of spectral, variational, and sampling methods for community detection in SBMs, highlighting their practical trade-offs.
Findings
Spectral methods like SCORE are fast and scalable.
Gibbs sampling performs best in small, well-separated networks.
Variational EM balances accuracy and efficiency in large, complex networks.
Abstract
The stochastic block model (SBM) is a fundamental tool for community detection in networks, yet the finite-sample performance of inference methods remains underexplored. We evaluate key algorithms-spectral methods, variational inference, and Gibbs sampling-under varying conditions, including signal-to-noise ratios, heterogeneous community sizes, and multimodality. Our results highlight significant performance variations: spectral methods, especially SCORE, excel in computational efficiency and scalability, while Gibbs sampling dominates in small, well-separated networks. Variational Expectation-Maximization strikes a balance between accuracy and cost in larger networks but struggles with optimization in highly imbalanced settings. These findings underscore the practical trade-offs among methods and provide actionable guidance for algorithm selection in real-world applications. Our…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Network Security and Intrusion Detection
