2011/12/23 by Wesley Nunes Gonçalves, Alexandre Souto Martinêz, Alexandre Souto Martinez +4 · 2 citations
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an) #cs.SI #physics.data-an #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1112.5625
openalex publication_date 2011/12/23 · arxiv created 2012/02/17 · arxiv updated 2012/02/20 · openalex created_date 2022/10/05 · openalex updated_date 2026/07/28
Complex networks have attracted increasing interest from various fields of science. It has been demonstrated that each complex network model presents specific topological structures which characterize its connectivity and dynamics. Complex network classification rely on the use of representative measurements that model topological structures. Although there are a large number of measurements, most of them are correlated. To overcome this limitation, this paper presents a new measurement for complex network classification based on partially self-avoiding walks. We validate the measurement on a data set composed by 40.000 complex networks of four well-known models. Our results indicate that the proposed measurement improves correct classification of networks compared to the traditional ones.