Network community detection via iterative edge removal in a flocking-like system

Verri, Filipe Alves Neto; Gueleri, Roberto Alves; Zheng, Qian; Zhang, Junbao; Zhao, Liang

doi:10.1140/epjs/s11734-021-00154-5

Cited by 1 publication

(2 citation statements)

References 28 publications

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“…Verri et al [12] present a network communitydetection technique based on properties that emerge from a nature-inspired flocking system. The proposed algorithm contains two alternating mechanisms: the control the particles alignment in higher dimensional space and an iterative process of edge removal.…”

Section: Theory and Methodsmentioning

confidence: 99%

“…The theoretical aspects considered here cover a broad spectrum of topics such as collective dynamics of excitable systems, cluster dynamics [1], interplay of noise and feedback [2], subdiffusive behavior [5], spreading phenomena on networks [8], coarsening [9], multipartite networks, partial synchrony [11], and time-delayed interactions [13,14]. In a series of works, complex networks are successfully employed as tools for detecting artificially inserted data [3], community detection [4,12], identification of time series patterns [6], and modeling a voter dynamics [15].…”

Section: Introductionmentioning

confidence: 99%

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Dynamical phenomena in complex networks: fundamentals and applications

Yanchuk

Roque

Macau

et al. 2021

Eur. Phys. J. Spec. Top.

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This special issue presents a series of 33 contributions in the area of dynamical networks and their applications. Part of the contributions is devoted to theoretical and methodological aspects of dynamical networks, such as collective dynamics of excitable systems, spreading processes, coarsening, synchronization, delayed interactions, and others. A particular focus is placed on applications to neuroscience and Earth science, especially functional climate networks. Among the highlights, various methods for dealing with noise and stochastic processes in neuroscience are presented. A method for constructing weighted networks with arbitrary topologies from a single dynamical node with delayed feedback is introduced. Also, a generalization of the concept of geodesic distances, a path-integral formulation of network-based measures is developed, which provides fundamental insights into the dynamics of disease transmission. The contributions from the Earth science application field substantiate predictive power of climate networks to study challenging Earth processes and phenomena.

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Section: Theory and Methodsmentioning

confidence: 99%