Wang Xiaofeng, Liu Gongshen, Li Jianhua, Nees Jan P
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
PLoS One. 2017 Jan 3;12(1):e0169355. doi: 10.1371/journal.pone.0169355. eCollection 2017.
Uncovering underlying community structures in complex networks has received considerable attention because of its importance in understanding structural attributes and group characteristics of networks. The algorithmic identification of such structures is a significant challenge. Local expanding methods have proven to be efficient and effective in community detection, but most methods are sensitive to initial seeds and built-in parameters. In this paper, we present a local expansion method by density-based clustering, which aims to uncover the intrinsic network communities by locating the structural centers of communities based on a proposed structural centrality. The structural centrality takes into account local density of nodes and relative distance between nodes. The proposed algorithm expands a community from the structural center to the border with a single local search procedure. The local expanding procedure follows a heuristic strategy as allowing it to find complete community structures. Moreover, it can identify different node roles (cores and outliers) in communities by defining a border region. The experiments involve both on real-world and artificial networks, and give a comparison view to evaluate the proposed method. The result of these experiments shows that the proposed method performs more efficiently with a comparative clustering performance than current state of the art methods.
由于在理解网络的结构属性和群体特征方面具有重要性，揭示复杂网络中潜在的社区结构受到了广泛关注。此类结构的算法识别是一项重大挑战。局部扩展方法在社区检测中已被证明是高效且有效的，但大多数方法对初始种子和内置参数敏感。在本文中，我们提出了一种基于密度聚类的局部扩展方法，旨在通过基于所提出的结构中心性定位社区的结构中心来揭示网络的内在社区。结构中心性考虑了节点的局部密度和节点之间的相对距离。所提出的算法通过单一局部搜索过程从结构中心向边界扩展社区。局部扩展过程遵循启发式策略，使其能够找到完整的社区结构。此外，它可以通过定义边界区域来识别社区中不同的节点角色（核心节点和离群点）。实验涉及真实世界网络和人工网络，并给出比较视图以评估所提出的方法。这些实验结果表明，所提出的方法在聚类性能上与当前最先进的方法相当，且执行效率更高。
