Attributed Graph Clustering: an Attribute-aware Graph Embedding Approach
Attributed Graph Clustering: an Attribute-aware Graph Embedding Approach
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DOI：
10.1145/3110025.3110092
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Esra Akbas;Peixiang Zhao
中科院分区：
文献类型：
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作者：
Esra Akbas;Peixiang Zhao
图聚类是社会网络分析中的一个基本问题，其目标是将图的顶点分组为一系列密集的簇，每个簇与所有其他簇分离。经典的图聚类方法利用图的拓扑结构来建模和量化顶点邻近度。随着丰富的图内容的激增，例如社交网络中的用户配置文件，以及蛋白质相互作用网络中的基因注释，对于高质量的图聚类，必须考虑图的结构和内容信息。在本文中，我们提出了一个图嵌入方法来聚类内容丰富的图。其核心思想是将图的每个顶点嵌入到一个连续的向量空间中，在这个空间中，顶点的局部结构和属性信息可以被编码在一个统一的潜在表示中。具体来说，我们将顶点属性接近度量化为边权重，并采用截断的属性感知随机游走来学习顶点的潜在表示。我们评估我们的属性感知图嵌入方法在现实世界中的属性图，结果表明，与最先进的算法相比，它的有效性。
Graph clustering is a fundamental problem in social network analysis, the goal of which is to group vertices of a graph into a series of densely knitted clusters with each cluster well separated from all the others. Classical graph clustering methods take advantage of the graph topology to model and quantify vertex proximity. With the proliferation of rich graph contents, such as user profiles in social networks, and gene annotations in protein interaction networks, it is essential to consider both the structure and content information of graphs for high-quality graph clustering. In this paper, we propose a graph embedding approach to clustering content-enriched graphs. The key idea is to embed each vertex of a graph into a continuous vector space where the localized structural and attributive information of vertices can be encoded in a unified, latent representation. Specifically, we quantify vertex-wise attribute proximity into edge weights, and employ truncated, attribute-aware random walks to learn the latent representations for vertices. We evaluate our attribute-aware graph embedding method in real-world attributed graphs, and the results demonstrate its effectiveness in comparison with state-of-the-art algorithms.