17,028 research outputs found
Community detection based on "clumpiness" matrix in complex networks
The "clumpiness" matrix of a network is used to develop a method to identify
its community structure. A "projection space" is constructed from the
eigenvectors of the clumpiness matrix and a border line is defined using some
kind of angular distance in this space. The community structure of the network
is identified using this borderline and/or hierarchical clustering methods. The
performance of our algorithm is tested on some computer-generated and
real-world networks. The accuracy of the results is checked using normalized
mutual information. The effect of community size heterogeneity on the accuracy
of the method is also discussed.Comment: 18 pages and 13 figure
Evidential Label Propagation Algorithm for Graphs
Community detection has attracted considerable attention crossing many areas
as it can be used for discovering the structure and features of complex
networks. With the increasing size of social networks in real world, community
detection approaches should be fast and accurate. The Label Propagation
Algorithm (LPA) is known to be one of the near-linear solutions and benefits of
easy implementation, thus it forms a good basis for efficient community
detection methods. In this paper, we extend the update rule and propagation
criterion of LPA in the framework of belief functions. A new community
detection approach, called Evidential Label Propagation (ELP), is proposed as
an enhanced version of conventional LPA. The node influence is first defined to
guide the propagation process. The plausibility is used to determine the domain
label of each node. The update order of nodes is discussed to improve the
robustness of the method. ELP algorithm will converge after the domain labels
of all the nodes become unchanged. The mass assignments are calculated finally
as memberships of nodes. The overlapping nodes and outliers can be detected
simultaneously through the proposed method. The experimental results
demonstrate the effectiveness of ELP.Comment: 19th International Conference on Information Fusion, Jul 2016,
Heidelber, Franc
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