图数据挖掘技术的研究与进展

生物信息学(蛋白质结构分析、基因组识别)、社会网络(实体间的联系)、Web分析(Web链接结构分析、Web内容挖掘和Web日志搜索)以及文本信息检索等的迅速发展积累了大量图数据，对于图数据的挖掘逐渐成为研究领域的热点。一些诸如聚类、分类、频繁模式挖掘的传统数据挖掘研究逐渐拓展到图数据领域。通过介绍现阶段图数据挖掘技术的研究进展，总结了图数据挖掘的特点、现实意义、主要问题以及应用场景，讨论并预测了图数据，尤其是不确定图数据研究的发展趋势和热点。

Research and advances on graph data mining

With the rapid growth of bioinformatics (protein structure analysis, genome identification), social networks (links between entities), Web analysis (interlinkage structure analysis, content mining and Web log retrieval), as well as the complex structure of text information retrievals, mining graph data has become a hot research field in recent years. Some traditional data mining algorithms have been gradually extended to graph data, such as clustering, classification, and frequent pattern mining. In this paper, the authors presented several state-of-art mainstream techniques for mining graph data, and gave a comprehensive summary of their characteristics, practical significance, as well as real-life applications on mining graph data. Finally, several research directions on graph data, and particularly, uncertain graph data were pointed out.