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Isolate sets partition benefits community detection of parallel Louvain method
Hang Qie1, Shijie Li2, Yong Dou1
1School of Computer, National University of Defense Technology, Changsha, 410073, China.
Scientific Reports
|May 17, 2022
Summary
This study introduces a novel graph partitioning algorithm for the parallel Louvain method (PLM) to address challenges in large-scale community detection. The new approach significantly improves speed and accuracy in identifying network structures.
Area of Science:
- Computer Science
- Network Analysis
- Algorithm Design
Background:
- Community detection is crucial for analyzing social networks and financial systems.
- The Louvain method is a popular heuristic for community detection but struggles with large-scale graphs.
- Existing parallel Louvain methods (PLM) face challenges with information synchronization latency and community swapping.
Purpose of the Study:
- To propose a novel graph partitioning algorithm to overcome limitations in parallel Louvain methods.
- To enhance the efficiency and accuracy of community detection on large-scale networks.
- To reduce latency and prevent community swapping in parallel graph analysis.
Main Methods:
- Developed a graph partitioning algorithm that divides graphs into 'isolate sets' for decoupled computation.
- Introduced an algorithm for creating isolate sets with a computation complexity similar to Breadth-First Search (BFS).
- Implemented an isolate-set-based parallel Louvain method (PLM) using OpenMP on an 8-core PC.
Main Results:
- The proposed parallel method achieved a maximum speedup of 4.62x compared to the sequential Louvain method.
- Higher modularity scores were obtained on 14 out of 18 tested graphs.
- The method effectively computed and updated vertex information without latency or community swapping.
Conclusions:
- The isolate-set-based graph partitioning algorithm effectively enhances the parallel Louvain method for large-scale community detection.
- This approach offers significant speedup and improved accuracy, making it suitable for complex network analysis.
- The method successfully addresses key challenges of latency and community swapping in parallel graph algorithms.
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