Identifying communities by influence dynamics in social networks

Communities are not static; they evolve, split and merge, appear and disappear, i.e. they are product of dynamical processes that govern the evolution of the network. A good algorithm for community detection should not only quantify the topology of the network, but incorporate the dynamical processes that take place on the network. We present a novel algorithm for community detection that combines network structure with processes that support creation and/or evolution of communities. The algorithm does not embrace the universal approach but instead tries to focus on social networks and model dynamic social interactions that occur on those networks. It identifies leaders, and communities that form around those leaders. It naturally supports overlapping communities by associating each node with a membership vector that describes node's involvement in each community. This way, in addition to overlapping communities, we can identify nodes that are good followers to their leader, and also nodes with no clear community involvement that serve as a proxy between several communities and are equally as important. We run the algorithm for several real social networks which we believe represent a good fraction of the wide body of social networks and discuss the results including other possible applications.Comment: 10 pages, 6 figure

Community detection for networks with unipartite and bipartite structure

Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network. The mixture network can be considered as a general case, with unipartite and bipartite networks viewed as its limiting cases. A mixture network can represent not only all the unipartite and bipartite networks, but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields such as biology and social science. Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B Ball et al (2011 Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks: a southern women bipartite network and a human transcriptional regulatory mixture network. The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.Comment: 27 pages, 8 figures. (http://iopscience.iop.org/1367-2630/16/9/093001

Overlapping Community Discovery Methods: A Survey

The detection of overlapping communities is a challenging problem which is gaining increasing interest in recent years because of the natural attitude of individuals, observed in real-world networks, to participate in multiple groups at the same time. This review gives a description of the main proposals in the field. Besides the methods designed for static networks, some new approaches that deal with the detection of overlapping communities in networks that change over time, are described. Methods are classified with respect to the underlying principles guiding them to obtain a network division in groups sharing part of their nodes. For each of them we also report, when available, computational complexity and web site address from which it is possible to download the software implementing the method.Comment: 20 pages, Book Chapter, appears as Social networks: Analysis and Case Studies, A. Gunduz-Oguducu and A. S. Etaner-Uyar eds, Lecture Notes in Social Networks, pp. 105-125, Springer,201

Communities in Networks

We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of community detection to problems in statistical physics and computational optimization.Comment: survey/review article on community structure in networks; published version is available at http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd

Deep Learning in Social Networks for Overlappering Community Detection

The collection of nodes is termed as community in any network system that are tightly associated to the other nodes. In network investigation, identifying the community structure is crucial task, particularly for exposing connections between certain nodes. For community overlapping, network discovery, there are numerous methodologies described in the literature. Numerous scholars have recently focused on network embedding and feature learning techniques for node clustering. These techniques translate the network into a representation space with fewer dimensions. In this paper, a deep neural network-based model for learning graph representation and stacked auto-encoders are given a nonlinear embedding of the original graph to learn the model. In order to extract overlapping communities, an AEOCDSN algorithm is used. The efficiency of the suggested model is examined through experiments on real-world datasets of various sizes and accepted standards. The method outperforms various well-known community detection techniques, according to empirical findings

A maximal clique based multiobjective evolutionary algorithm for overlapping community detection

Detecting community structure has become one im-portant technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can be-long to only one community. However, in many real-world net-works, communities are often overlapped with each other. De-veloping overlapping community detection algorithms thus be-comes necessary. Along this avenue, this paper proposes a maxi-mal clique based multiobjective evolutionary algorithm for over-lapping community detection. In this algorithm, a new represen-tation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme al-lows multiobjective evolutionary algorithms to handle the over-lapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm