Detecting the communities in directed networks is a challenging task. Many of the existing community detection algorithm are designed to disclose the community structure for undirected networks. These algorithms can be applied to directed networks by transforming the directed networks to undirected. However, ignoring the direction of the links loses the information concealed along the link and end-up with imprecise community structure. In this paper, we retain the direction of the graph and propose a cooperative game in order to capture the interactions among the nodes of the network. We develop a greedy community detection algorithm to disclose the overlapping communities of the given directed network. Experimental evaluation on synthetic networks illustrates that the algorithm is able to disclose the correct number of communities with good community structure. © 2018, Springer International Publishing AG, part of Springer Nature.

Annapurna Jonnalagadda

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Lakshmanan K

Department of Analytics

Lakshmanan Kuppusamy

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Mining Communities in Directed Networks: A Game Theoretic Approach

Advances in Intelligent Systems and Computing Intelligent Systems Design and Applications

Community detection in social networks is a challenging and complex task, which received much attention from researchers of multiple domains in recent years. The evolution of communities in social networks happens merely due to the self-interest of the nodes. The interesting feature of community structure in social networks is the multi membership of the nodes resulting in overlapping communities. Assuming the nodes of the social network as self-interested players, the dynamics of community formation can be captured in the form of a game. In this paper, we propose a greedy algorithm, namely, Weighted Graph Community Game (WGCG), in order to model the interactions among the self-interested nodes of the social network. The proposed algorithm employs the Shapley value mechanism to discover the inherent communities of the underlying social network. The experimental evaluation on the real-world and synthetic benchmark networks demonstrates that the performance of the proposed algorithm is superior to the state-of-the-art overlapping community detection algorithms. © 2017 Elsevier B.V.

Physica A: Statistical Mechanics and its Applications

A cooperative game framework for detecting overlapping communities in social networks

Community detection in social networks has received much attention from the researchers of multiple disciplines due to its impactful applications such as recommendation systems, link prediction, and anomaly detection. The focus of community detection is to determine the more dense subgraphs of the network which are called communities. The nodes of the community are expected to have similar features and interests. Assuming the nodes as selfish agents, the evolution of communities can be effectively modelled as a community formation game. Game theory provides a systematic framework to model the competition and coordination among the players. In the past decade, there are several contributions from the domain of game theory to address the problem of community detection in social networks. In this paper, we make a comprehensive survey that studies and provides an insight into available game theory-based community detection algorithms. The current study provides the taxonomy of game models and their characteristics along with their performance. We discuss the interesting applications of game theory for social networks and also provide further research directions as well as some open challenges. © 2016, Springer-Verlag Wien.

Journal	Data powered by TypesetAdvances in Intelligent Systems and Computing Intelligent Systems Design and Applications
Publisher	Data powered by TypesetSpringer International Publishing
ISSN	2194-5357
Open Access	No