Extended version of arXiv:0812.1770, 'Laplacian Dynamics and Multiscale Modular Structure in Networks', by the same authors, with new content; published in Transactions on Network Science and Engineering; 16 pages; 11 figs
发表信息：
IEEE Transactions on Network Science and Engineering (Volume:1 , Issue: 2 ) pp 76-90, 2015
Most methods proposed to uncover communities in complex networks rely on combinatorial graph properties. Usually an edge-counting quality function, such as modularity, is optimized over all partitions of the graph compared against a null random graph model. Here we introduce a systematic dynamical framework to design and analyze a wide variety of quality functions for community detection. The quality of a partition is measured by its Markov Stability, a time-parametrized function defined in terms of the statistical properties of a Markov process taking place on the graph. The Markov process provides a dynamical sweeping across all scales in the graph, and the time scale is an intrinsic parameter that uncovers communities at different resolutions.
This dynamic-based community detection leads to a compound optimization, which favours communities of comparable centrality (as defined by the stationary distribution), and provides a unifying framework for spectral algorithms, as well as different heuristics for community detection, including versions of modularity and Potts model. Our dynamic framework creates a systematic link between different stochastic dynamics and their corresponding notions of optimal communities under distinct (node and edge) centralities. We show that the Markov Stability can be computed efficiently to find multi-scale community structure in large networks.
Two-mode geometry controls multiscale organization in bipartite systems
University of Rome Tor Vergata（罗马托尔维尔加塔大学）；Enrico Fermi Research Center (CREF)（恩里科·费米研究中心）；INFN（意大利国家核物理研究所）；Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada（格拉纳达大学卡洛斯一世理论与计算物理研究所）
Discriminating word senses with tourist walks in complex networks
Institute of Mathematics；Computer Science University of S\ ao Paulo, P. O. Box 369, Postal Code 13560-970 S\ ao Carlos, S\ ao Paulo, Brazil Institute of Physics of S\ ao Carlos University of S\ ao Paulo, P. O. Box 369, Postal Code 13560-970 S\ ao Carlos, S\ ao Paulo, Brazil
Switcher-random-walks: a cognitive-inspired mechanism for network exploration
University of Navarra（纳瓦拉大学）；Center for Applied Medical Research（应用医学研究中心）；ICREA-Complex Systems Lab（ICREA复杂系统实验室）；Universitat Pompeu Fabra（庞培法布拉大学）；Institut d’investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)（奥古斯特·皮·苏涅尔生物医学研究所（IDIBAPS））；Hospital Clínic（临床医院）
