Browsing by Author "Eustace, Justine"

Now showing 1 - 6 of 6
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    Adaptive synchronization in complex networks with non-delay and variable delay couplings via pinning control
    (Elsevier, 2014) Liang, Yi; Wang, Xingyuan; Eustace, Justine
    The adaptive pinning synchronization in complex networks with non-delay and variable delay couplings is investigated in this letter. For both reducible and irreducible coupling matrixes, novel and practical synchronization criteria are obtained by Lasalle's invariance principle, and sufficient conditions for achieving the synchronization to require the minimum number of pinning nodes are derived. Moreover, the method for calculating the number of pinning nodes is given using the decreasing law of maximum eigenvalues of the principal submatrixes. At last, simulation examples are given to verify effectiveness of the proposed pinning synchronization scheme.
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    Approximating web communities using subspace decomposition
    (Elsevier, 2014) Eustace, Justine; Wang, Xingyuan; Li, Junqiu
    Herein, we propose an algorithm to approximate web communities from the topic related web pages. The approximation is achieved by subspace factorization of the topic related web pages. The factorization process reveals existing association between web pages such that the closely related web pages are extracted. We vary the approximation values to identify varied degrees of relationship between web pages. Experiments on real data sets show that the proposed algorithm reduces the impact of unrelated links and therefore can be used to control spam links in web pages.
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    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.
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    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.
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    Detecting overlapping communities by seed community in weighted complex networks
    (Elsevier, 2013) Li, Junqiu; Wang, Xingyuan; Eustace, Justine
    Detection of community structures in the weighted complex networks is significant to understand the network structures and analysis of the network properties. We present a unique algorithm to detect overlapping communities in the weighted complex networks with considerable accuracy. For a given weighted network, all the seed communities are first extracted. Then to each seed community, more community members are absorbed using the absorbing degree function. In addition, our algorithm successfully finds common nodes between communities. The experiments using some real-world networks show that the performance of our algorithm is satisfactory.
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    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.