Book ChapterDOI
Mining Communities in Directed Networks: A Game Theoretic Approach
Annapurna Jonnalagadda,Lakshmanan Kuppusamy +1 more
- pp 826-835
TLDR
This paper develops a greedy community detection algorithm to disclose the overlapping communities of the given directed network and proposes a cooperative game in order to capture the interactions among the nodes of the network.Abstract:
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.read more
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Book
Social Network Analysis: Methods and Applications
TL;DR: This paper presents mathematical representation of social networks in the social and behavioral sciences through the lens of Dyadic and Triadic Interaction Models, which describes the relationships between actor and group measures and the structure of networks.
Journal ArticleDOI
Community detection in graphs
TL;DR: A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists.
Journal ArticleDOI
Community detection in graphs
TL;DR: A thorough exposition of the main elements of the clustering problem can be found in this paper, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
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TL;DR: This exciting and pioneering new overview of multiagent systems, which are online systems composed of multiple interacting intelligent agents, i.e., online trading, offers a newly seen computer science perspective on multi agent systems, while integrating ideas from operations research, game theory, economics, logic, and even philosophy and linguistics.
Journal ArticleDOI
Community Structure in Directed Networks
Elizabeth Leicht,Mark Newman +1 more
TL;DR: This work describes an explicit algorithm based on spectral optimization of the modularity and shows that it gives demonstrably better results than previous methods on a variety of test networks, both real and computer generated.