Detecting communities in large networks

Abstract

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and links orientations. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable to the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.

A complex network based model for detecting isolated communities in water distribution networks

“…In Ref. 49, the Capocci-Servedio-Colaiori-Caldarelli method combines spectral properties of networks with correlation measurements to detect the closeness of communities. For further discussions of some methods mentioned above, the readers are referred to a comprehensive review as given in Ref.…”

“…51 Some previous researches have studied the spectral properties of graph that can be useful for isolated community detection. 49,52 In the case of WDN, spectral analysis based on Laplacian matrix can be a new and effective approach for fast and accurate detection of isolated communities.…”

Interaction-Driven Self-adaptation of Service Ensembles

“…One important structural property of service ensembles is the number and the size of subgroups that emerge from the interaction distance graph and what factors cause this structure. Graph analysis based on interaction affinities (e.g., [19]) or community detection algorithms (e.g., [20] or [21]) describes the underlying structure from which we subsequently derive appropriate system requirements. Fig.…”

Synchronization processes in complex networks

“…The Laplacian matrix is defined as L ij = k i δ ij − a ij , where k i is the degree of node i, δ ij is the Kronecker delta and a ij is the element of the adjacency matrix (1 if nodes i and j are connected and 0 otherwise). The spectral information of the Laplacian matrix has been used to understand the structure of complex networks [41], and in particular to detect the community structure [42,43] (also the spectral analysis of the modularity matrix [44] can be used to this end). Recent studies have also focused on the spectral information of the Laplacian matrix and the synchronization dynamics [23,24,25,26,27,28,29,30].…”