KCoreMotif：一种利用k-核分解和模体的大规模网络图聚类算法
KCoreMotif: An Efficient Graph Clustering Algorithm for Large Networks by Exploiting k-core Decomposition and Motifs
摘要：
聚类分析在无线传感器网络、在线社交网络等复杂网络的信任评估中得到了广泛的应用。谱聚类是图结构数据（网络）最常用的算法之一。然而，传统的谱聚类本质上是难以与大规模的网络，由于它需要计算昂贵的矩阵操作的事实。为了处理大型网络，在本文中，我们提出了一个有效的图聚类算法，KCoreMotif，专门针对大型网络，利用k-核心分解和图案。所提出的聚类算法背后的基本思想是在k-核心子图上执行高效的基于模体的谱聚类算法，而不是在整个图上。更具体地说，（1）我们首先对大输入网络进行k-核心分解;（2）然后对顶部k-核心子图进行基于模体的谱聚类;（3）将其余（k-1）-核心子图中的剩余顶点分组到先前找到的聚类中;最后获得大输入网络的期望聚类。为了评估所提出的图聚类算法KCoreMotif的性能，我们使用传统的和基于模体的谱聚类算法作为基线，并比较我们的算法与他们的18组真实世界的数据集。比较结果表明，该算法对大型网络具有较高的准确性和有效性，可进一步用于评估大型网络的簇内和簇间信任度。
英文摘要：
Clustering analysis has been widely used in trust evaluation on various complex networks such as wireless sensors networks and online social networks. Spectral clustering is one of the most commonly used algorithms for graph-structured data (networks). However, the conventional spectral clustering is inherently difficult to work with large-scale networks due to the fact that it needs computationally expensive matrix manipulations. To deal with large networks, in this paper, we propose an efficient graph clustering algorithm, KCoreMotif, specifically for large networks by exploiting k-core decomposition and motifs. The essential idea behind the proposed clustering algorithm is to perform the efficient motif-based spectral clustering algorithm on k-core subgraphs, rather than on the entire graph. More specifically, (1) we first conduct the k-core decomposition of the large input network; (2) we then perform the motif-based spectral clustering for the top k-core subgraphs; (3) we group the remaining vertices in the rest (k-1)-core subgraphs into previously found clusters; and finally obtain the desired clusters of the large input network. To evaluate the performance of the proposed graph clustering algorithm KCoreMotif, we use both the conventional and the motif-based spectral clustering algorithms as the baselines and compare our algorithm with them for 18 groups of real-world datasets. Comparative results demonstrate that the proposed graph clustering algorithm is accurate yet efficient for large networks, which also means that it can be further used to evaluate the intra-cluster and inter-cluster trusts on large networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
