SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process (1109.5720v3)

Abstract: Overlap is one of the characteristics of social networks, in which a person may belong to more than one social group. For this reason, discovering overlapping structures is necessary for realistic social analysis. In this paper, we present a novel, general framework to detect and analyze both individual overlapping nodes and entire communities. In this framework, nodes exchange labels according to dynamic interaction rules. A specific implementation called Speaker-listener Label Propagation Algorithm (SLPA1) demonstrates an excellent performance in identifying both overlapping nodes and overlapping communities with different degrees of diversity.

• The paper introduces a novel SLPA that extends traditional label propagation by allowing nodes to hold multiple labels for overlapping community detection.
• It employs a dynamic speaker-listener interaction with probabilistic label exchange and threshold-based post-processing to ensure stability and accuracy.
• Experimental results show that SLPA outperforms existing methods in precision, recall, and computational efficiency on both synthetic and real-world networks.

SLPA: A Dynamic Process for Overlapping Community Detection in Social Networks

The paper "SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process" presents an innovative approach to identifying overlapping communities within social networks. The authors, Jierui Xie, Boleslaw K. Szymanski, and Xiaoming Liu, propose the Speaker-listener Label Propagation Algorithm (SLPA) as a framework for detecting and analyzing overlapping structures within networks.

Methodological Framework

SLPA extends traditional Label Propagation Algorithms (LPA) by allowing nodes to hold multiple labels, facilitating the identification of overlapping communities. The methodology is driven by a dynamic interaction process inspired by human communication, where nodes alternate between speaker and listener roles. Nodes accumulate observed labels rather than erasing old data, thus improving both stability and accuracy in detecting overlaps.

The algorithm orchestrates its operations over three main stages:

1. Initialization: Nodes are initially assigned unique labels.
2. Dynamic Evolution: Through iterative interactions, neighboring nodes exchange labels based on probabilistic rules.
3. Post-processing: Collected label data is transformed into community assignments using a predefined threshold, providing a balance between labeled nodes and community detection precision.

SLPA employs an asynchronous update mechanism, ensuring efficiency and robustness by incorporating both current and past observations of a node's neighborhood.

Numerical Results and Comparative Analysis

In evaluating SLPA, the authors conducted experiments on both synthetic and real-world networks to assess its performance against existing algorithms like CFinder, Copra, and LFM. The results reveal that SLPA consistently achieves superior performance in identifying overlapping nodes and communities.

• F-score and NMI: SLPA demonstrated high precision and recall rates in node classification and achieved notable scores in normalized mutual information (NMI) across varying network structures.
• Execution Time: With its computational complexity of $O(Tn)$ in sparse networks, SLPA exhibits efficient scalability.
• Stability: The algorithm's outputs remain stable across diverse parameter settings, indicating its resilience against variations in network size or overlap degree.

Implications and Future Directions

SLPA's adaptable framework presents both theoretical and practical implications in community detection. It highlights the significance of incorporating dynamic interaction models in algorithms to mimic realistic network behaviors more closely. This approach opens avenues for further developments in detecting fuzzy hierarchies and temporal communities.

The research represents a step forward in network analysis, offering an effective tool for examining complex social systems. Future work could explore SLPA's potential adaptations, particularly in non-static or time-evolving networks, further enhancing its applicability across varying domains of social network analysis.

This paper contributes significantly to our understanding and methodology for detecting and analyzing overlapping communities, presenting a robust and efficient approach with widespread applicability in social network analysis and beyond.