There has been an increasing interest in properties of complex networks, such as small-world property, power-law degree distribution, and network transitivity which seem to be common to many real world networks. In this study, a useful community detection method based on non-negative matrix factorization (NMF) technique is presented. Based on a popular modular function, a proper feature matrix ...

Complex networks are generally composed of dense sub-networks called communities. Many algorithms have been proposed to automatically detect such communities. However, they are often unstable and behave nondeterministically. We propose here to use this non-determinism in order to compute groups of nodes on which community detection algorithms agree most of the time. We show that these groups of...

Community structure is an important aspect of network analysis, with a variety of reallife applications. Local community detection algorithms, which are relatively new in literature, provide the opportunity to analyze community structure in large networks without needing global information. We focus our work on a state-of-the-art algorithm developed by Yang and Leskovec and evaluate it on three...

Michele Tumminello, 2 Salvatore Miccichè, Fabrizio Lillo, 3 Jan Varho, Jyrki Piilo, and Rosario N. Mantegna Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Dipartimento di Fisica e Tecnologie Relative, Università di Palermo, viale delle Scienze Ed.18, I-90128 Palermo, Italia Santa Fe Institute, 1399 Hyde Park Road, Santa Fe NM 87501, USA Turku ...

Recent researches on complex systems highlighted the so-called super-linear growth phenomenon. As the system size P measured as population in cities or active users in online communities increases, the total activities X measured as GDP or number of new patents, crimes in cities generated by these people also increases but in a faster rate. This accelerating growth phenomenon can be well descri...

Many complex systems can be represented as complex networks. Among them there has been much interest on community structure recently, and many studies focus attention on it, in particular community detection. While community detection can provide us much information, the community structure implies another feature, hierarchy of the system. Coarse-graining of complex networks can lead us to the ...

Many real-world complex systems such as social, biological, information as well as technological systems results of a decentralized and unplanned evolution which leads to a common structuration. Irrespective of their origin, these so-called complex networks typically exhibit small-world and scalefree properties. Another common feature is their organisation into communities. In this paper, we in...

Identifying strongly associated clusters in large complex networks has received an increased amount of interest since the past decade. The problem of community detection in complex networks is an NP complete problem that necessitates the clustering of a network into communities of compactly linked nodes in such a manner that the interconnection between the nodes is found to be denser than the i...