By Hua-Wei Shen (auth.)
Community constitution is a salient structural attribute of many real-world networks. groups are more often than not hierarchical, overlapping, multi-scale and coexist with different kinds of structural regularities of networks. This poses significant demanding situations for traditional tools of group detection. This publication will comprehensively introduce the newest advances in group detection, particularly the detection of overlapping and hierarchical neighborhood buildings, the detection of multi-scale groups in heterogeneous networks, and the exploration of a number of varieties of structural regularities. those advances were effectively utilized to investigate large-scale on-line social networks, reminiscent of fb and Twitter. This publication presents readers a handy solution to seize the innovative of group detection in complicated networks.
The thesis on which this publication is predicated was once commemorated with the “Top a hundred first-class Doctoral Dissertations Award” from the chinese language Academy of Sciences and was once nominated because the “Outstanding Doctoral Dissertation” through the chinese language laptop Federation.
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Additional resources for Community Structure of Complex Networks
1(d) shows the hierarchical and overlapping community structure found by the algorithm EAGLE. We can see that the algorithm EAGLE provides a possible way to investigate a more complete picture of the community structure. Now we turn to the basic ideas behind the algorithm EAGLE. Generally speaking, a community can be regarded as a node set within which the nodes are more likely connected to each other than to the rest of the network. This indicates that a community usually has relatively high link-density.
USA 101, 2658–2663 (2004) 11. : Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004) 12. : Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103, 8577–8582 (2006) 13. : Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007) 14. : Comparing community structure identification. J. Stat. Mech. P09008 (2005) 15. : Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004) 16.
The network splits naturally into two groups, represented by the squares and circles in Fig. 11. By applying our method with k = 4 to this network, four communities are obtained, denoted by different colors in Fig. 11. The green community is connected loosely to the other three ones. Regarding the three circle-denoted communities as a sole community, it and the green community correspond to the known division observed by Lusseau . Furthermore, the three circle-denoted communities also correspond to a real division among these dolphins.