Abstract
For an undirected complex network made up with vertices and edges, we developed a fast computing algorithm that divides vertices into different groups by maximizing the standard “modularity” measure of the resulting partitions. The algorithm is based on a simple constrained power method which maximizes a quadratic objective function while satisfying given linear constraints. We evaluated the performance of the algorithm and compared it with a number of state-of-the-art solutions. The new algorithm reported both high optimization quality and fast running speed, and thus it provided a practical tool for community detection and network structure analysis.
Highlights
Many real-world complex systems can be formulated as networks, with entities represented by vertices and relationships represented by edges
“how many communities are there and what are the memberships” for each vertex [3]. Such community structures are commonly believed to exist in networks: vertices tend to cluster in groups with dense edge connections while the edge connections are sparser between vertices from different groups
We systematically evaluated the proposed iterative roundingbased spectral method (IR) and compared its performance with the conventional rounding-based spectral method (CR), simulated annealing (SA) [15], the linear programming method (LP) [10], and a multilevel optimization method (ML) [14], which have achieved state-of-the-art optimization quality in different applications
Summary
Many real-world complex systems can be formulated as networks, with entities represented by vertices and relationships represented by edges. “how many communities are there and what are the memberships” for each vertex [3] Such community structures are commonly believed to exist in networks: vertices tend to cluster in groups with dense edge connections while the edge connections are sparser between vertices from different groups. To automate the process of community detection in complex networks, a well-known technique is often adopted through maximizing the “modularity” measure proposed by Girvan and Newman [4]. This model quantifies the quality of a particular vertex partition of a network with a single numeric value. We show our evaluation results with improved results, and draw the conclusion
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