Detecting community structure in networks
نویسنده
چکیده
There has been considerable recent interest in algorithms for finding communities in networks— groups of vertices within which connections are dense, but between which connections are sparser. Here we review the progress that has been made towards this end. We begin by describing some traditional methods of community detection, such as spectral bisection, the Kernighan–Lin algorithm and hierarchical clustering based on similarity measures. None of these methods, however, is ideal for the types of real-world network data with which current research is concerned, such as Internet and web data and biological and social networks. We describe a number of more recent algorithms that appear to work well with these data, including algorithms based on edge betweenness scores, on counts of short loops in networks and on voltage differences in resistor networks. PACS. 89.75.Hc Networks and genealogical trees – 87.23.Ge Dynamics of social systems – 89.20.Hh World Wide Web, Internet – 05.10.-a Computational methods in statistical physics and nonlinear dynamics
منابع مشابه
Detecting Overlapping Communities in Social Networks using Deep Learning
In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...
متن کاملMining Overlapping Communities in Real-world Networks Based on Extended Modularity Gain
Detecting communities plays a vital role in studying group level patterns of a social network and it can be helpful in developing several recommendation systems such as movie recommendation, book recommendation, friend recommendation and so on. Most of the community detection algorithms can detect disjoint communities only, but in the real time scenario, a node can be a member of more than one ...
متن کاملOverview of Algorithms for Detecting Community Structure in Complex Networks
Community structure is a very important property of complex networks. Detecting communities in networks is of great importance in biology, computer science, sociology and so on. In recent years, a lot of community discovery algorithms have been proposed aiming at different kinds of large scale complex networks. In this paper, we review some latest representative algorithms, focusing on the impr...
متن کاملAn Algorithm for Detecting Community Structure of Complex Networks based on Clustering
There are considerable interest in algorithms for detecting community structure, which is fundamental for analyzing the relationship between structure and function in complex networks. In this paper, after the introduction of some traditional approaches for detecting community structure and data mining clustering algorithms, we propose Mapping Vertex into Vector(MVV) algorithm, which can conver...
متن کاملA Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks
In this paper a simple but efficient real-time detecting algorithm is proposed for tracking community structure of dynamic networks. Community structure is intuitively characterized as divisions of network nodes into subgroups, within which nodes are densely connected while between which they are sparsely connected. To evaluate the quality of community structure of a network, a metric called mo...
متن کاملThe Concentration and Stability of the 1 Community Detecting Functions on Random 2 Networks
6 We propose a general form of community detecting functions for find7 ing the communities or the optimal partition of a random network, and 8 examine the concentration and stability of the function values using the 9 bounded difference martingale method. We derive LDP inequalities for 10 both the general case and several specific community detecting functions: 11 modularity, graph bipartitioni...
متن کامل