基于边信息的图流聚类研究
On Graph Stream Clustering with Side Information
摘要：
由于网络、社交网络和生物信息学等新兴应用，图聚类成为一个重要的问题。近来，许多这样的应用以流的形式生成数据。由于图的复杂结构和连续数据的计算困难，聚类大规模的动态图流是非常具有挑战性的。同时，大量的边信息与图相关联，其可以是各种类型。这些例子包括用户在社交网络活动中的属性、与web点击图流相关联的Meta属性以及移动通信网络中的位置信息。这些属性包含非常有用的信息，并有可能改善聚类过程，但被最近的图流挖掘技术所忽视。在本文中，我们定义了一个统一的距离度量的链接结构和侧属性的聚类。此外，我们提出了一个新的优化框架DMO，它可以动态优化的距离度量，使其适应新接收的流数据。我们进一步介绍了一个精心设计的统计SGS（C），消耗恒定的存储空间的流的进展。我们证明，保持的统计数据是足够的聚类过程，以及距离优化，可以扩展到大量的图形与侧属性。我们将提出实验结果，以显示该方法在图流聚类与链接和边信息超过基线的优势。
英文摘要：
Graph clustering becomes an important problem due to emerging applications involving the web, social networks and bio-informatics. Recently, many such applications generate data in the form of streams. Clustering massive, dynamic graph streams is significantly challenging because of the complex structures of graphs and computational difficulties of continuous data. Meanwhile, a large volume of side information is associated with graphs, which can be of various types. The examples include the properties of users in social network activities, the meta attributes associated with web click graph streams and the location information in mobile communication networks. Such attributes contain extremely useful information and has the potential to improve the clustering process, but are neglected by most recent graph stream mining techniques. In this paper, we define a unified distance measure on both link structures and side attributes for clustering. In addition, we propose a novel optimization framework DMO, which can dynamically optimize the distance metric and make it adapt to the newly received stream data. We further introduce a carefully designed statistics SGS(C) which consume constant storage spaces with the progression of streams. We demonstrate that the statistics maintained are sufficient for the clustering process as well as the distance optimization and can be scalable to massive graphs with side attributes. We will present experiment results to show the advantages of the approach in graph stream clustering with both links and side information over the baselines.
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。
