Community detection in networks using self-avoiding random walks
| Autor: | José Ricardo Furlan Ronqui, Guilherme de Guzzi Bagnato, Gonzalo Travieso |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Physics - Physics and Society Strongly connected component Optimization problem Theoretical computer science Computational complexity theory Computer science Open problem FOS: Physical sciences Physics and Society (physics.soc-ph) 01 natural sciences Modularity 010305 fluids & plasmas 0103 physical sciences 010306 general physics INTERNET Condensed Matter - Statistical Mechanics Social and Information Networks (cs.SI) Statistical Mechanics (cond-mat.stat-mech) Computer Science - Social and Information Networks Complex network Condensed Matter Physics Random walk Vertex (geometry) Principal component analysis |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | Different kinds of random walks have proven to be useful in the study of structural properties of complex networks. Among them, the restricted dynamics of self-avoiding random walks (SAW), which visit only at most once each vertex in the same walk, has been successfully used in network exploration. The detection of communities of strongly connected vertices in networks remains an open problem, despite its importance, due to the high computational complexity of the associated optimization problem and the lack of a unique formal definition of communities. In this work, we propose a SAW-based method to extract the community distribution of a network and show that it achieves high modularity scores, specially for real-world networks. We combine SAW with principal component analysis to define the dissimilarity measure to be used for agglomerative hierarchical clustering. To evaluate the performance of this method we compare it with four popular methods for community detection: Girvan-Newman, Fastgreedy, Walktrap and Infomap using two types of synthetic networks and six well-known real-world cases. 10 pages, 7 figures and 1 table |
| Databáze: | OpenAIRE |
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