IEEE Trans Cybern. 2019 Jan;49(1):247-260. doi: 10.1109/TCYB.2017.2771496. Epub 2017 Nov 22.
Attributed graphs have attracted much attention in recent years. Different from conventional graphs, attributed graphs involve two different types of heterogeneous information, i.e., structural information, which represents the links between the nodes, and attribute information on each of the nodes. Clustering on attributed graphs usually requires the fusion of both types of information in order to identify meaningful clusters. However, most of existing works implement the combination of these two types of information in a "global" manner by treating all nodes equally and learning a global weight for the information fusion. To address this issue, this paper proposed a novel weighted K -means algorithm with "local" learning for attributed graph clustering, called adaptive fusion of structural and attribute information (Adapt-SA) and analyzed the convergence property of the algorithm. The key advantage of this model is to automatically balance the structural connections and attribute information of each node to learn a fusion weight, and get densely connected clusters with high attribute semantic similarity. Experimental study of weights on both synthetic and real-world data sets showed that the weights learned by Adapt-SA were reasonable, and they reflected which one of these two types of information was more important to decide the membership of a node. We also compared Adapt-SA with the state-of-the-art algorithms on the real-world networks with varieties of characteristics. The experimental results demonstrated that our method outperformed the other algorithms in partitioning an attributed graph into a community structure or other general structures.
近年来，有向图受到了广泛关注。与传统图不同，有向图涉及两种不同类型的异质信息，即结构信息，表示节点之间的链接，以及每个节点上的属性信息。在有向图上进行聚类通常需要融合这两种信息，以识别有意义的聚类。然而，大多数现有工作通过平等对待所有节点并为信息融合学习全局权重，以“全局”的方式实现这两种类型信息的组合。针对这个问题，本文提出了一种新颖的有向图聚类加权 K-均值算法，称为自适应融合结构和属性信息（Adapt-SA），并分析了算法的收敛性。该模型的主要优点是能够自动平衡每个节点的结构连接和属性信息，以学习融合权重，并获得具有高属性语义相似性的密集连接聚类。在合成和真实数据集上的权重实验研究表明，Adapt-SA 学习到的权重是合理的，它们反映了这两种信息类型中哪一种对决定节点的归属更为重要。我们还将 Adapt-SA 与具有各种特征的真实网络上的最新算法进行了比较。实验结果表明，我们的方法在将有向图划分为社区结构或其他一般结构方面优于其他算法。
