Browsing by Author "Cui, Yaozu"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Community detection using local neighborhood in complex networks
    (Elsevier, 2015) Eustace, Justine; Wang, Xingyuan; Cui, Yaozu
    It is common to characterize community structure in complex networks using local neighborhood. Existing related methods fail to estimate the accurate number of nodes present in each community in the network. In this paper a community detection algorithm using local community neighborhood ratio function is proposed. The proposed algorithm predicts vertex association to a specific community using visited node overlapped neighbors. In the beginning, the algorithm detects local communities; then through iterations and local neighborhood ratio function, final communities are detected by merging close related local communities. Analysis of simulation results on real and artificial networks shows the proposed algorithm detects well defined communities in both networks by wide margin.
  • Thumbnail Image
    Item
    Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks
    (Elsevier, 2014) Cui, Yaozu; Wang, Xingyuan; Eustace, Justine
    Community structure is a common phenomenon in complex networks, and it has been shown that some communities in complex networks often overlap each other. So in this paper we propose a new algorithm to detect overlapping community structure in complex networks. To identify the overlapping community structure, our algorithm firstly extracts fully connected sub-graphs which are maximal sub-graphs from original networks. Then two maximal sub-graphs having the key pair-vertices can be merged into a new larger sub-graph using some belonging degree functions. Furthermore we extend the modularity function to evaluate the proposed algorithm. In addition, overlapping nodes between communities are founded successfully. Finally we report the comparison between the modularity and the computational complexity of the proposed algorithm with some other existing algorithms. The experimental results show that the proposed algorithm gives satisfactory results.
  • Thumbnail Image
    Item
    Overlapping community detection using neighborhood ratio matrix
    (Elsevier, 2015) Eustace, Justine; Wang, Xingyuan; Cui, Yaozu
    The participation of a node in more than one community is a common phenomenon in complex networks. However most existing methods, fail to identify nodes with multiple community affiliation, correctly. In this paper, a unique method to define overlapping community in complex networks is proposed, using the overlapping neighborhood ratio to represent relations between nodes. Matrix factorization is then utilized to assign nodes into their corresponding community structures. Moreover, the proposed method demonstrates the use of Perron clusters to estimate the number of overlapping communities in a network. Experimental results in real and artificial networks show, with great accuracy, that the proposed method succeeds to recover most of the overlapping communities existing in the network.