Mining Knowledge from Data: An Information Network Analysis Approach
Mining Knowledge from Data: An Information Network Analysis Approach
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DOI：
10.1109/icde.2012.145
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发表时间：
2012-04
期刊：
影响因子：
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通讯作者：
Jiawei Han;Yizhou Sun;Xifeng Yan;Philip S. Yu
中科院分区：
文献类型：
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作者：
Jiawei Han;Yizhou Sun;Xifeng Yan;Philip S. Yu
真实的世界中的大多数对象和数据是相互关联的，形成复杂的、异构的但通常是半结构化的信息网络。然而，许多数据库研究人员认为数据库仅仅是一个支持存储和检索的数据仓库，而不是一个支持全面数据分析的信息丰富，相互关联和多类型的信息网络，而许多网络研究人员则专注于同构网络。从这两个出发，我们认为互联的，半结构化的数据集异构的，信息丰富的网络和研究如何发现隐藏在这样的网络知识。例如，一个大学数据库可以被看作是一个异构的信息网络，其中多种类型的对象，如学生，教授，课程，部门，和多种类型的关系，如教和建议交织在一起，提供丰富的信息。在本教程中，我们提出了一个有组织的图片挖掘异构信息网络，并介绍了一组有趣的，有效的和可扩展的网络挖掘方法。所涵盖的主题包括：（i）数据库作为一个信息网络，（ii）挖掘信息网络：聚类，分类，排名，相似性搜索和Meta路径引导分析，（iii）建设质量，信息网络的数据挖掘，（iv）在异构信息网络的趋势和演变分析，和（v）研究前沿。我们发现，异构信息网络是信息丰富的，在这种网络上的链接分析是强大的发现隐藏在大型半结构化数据集的关键知识。最后，我们还提出了一些有前途的研究方向。
Most objects and data in the real world are interconnected, forming complex, heterogeneous but often semistructured information networks. However, many database researchers consider a database merely as a data repository that supports storage and retrieval rather than an information-rich, inter-related and multi-typed information network that supports comprehensive data analysis, whereas many network researchers focus on homogeneous networks. Departing from both, we view interconnected, semi-structured datasets as heterogeneous, information-rich networks and study how to uncover hidden knowledge in such networks. For example, a university database can be viewed as a heterogeneous information network, where objects of multiple types, such as students, professors, courses, departments, and multiple typed relationships, such as teach and advise are intertwined together, providing abundant information. In this tutorial, we present an organized picture on mining heterogeneous information networks and introduce a set of interesting, effective and scalable network mining methods. The topics to be covered include (i) database as an information network, (ii) mining information networks: clustering, classification, ranking, similarity search, and meta path-guided analysis, (iii) construction of quality, informative networks by data mining, (iv) trend and evolution analysis in heterogeneous information networks, and (v) research frontiers. We show that heterogeneous information networks are informative, and link analysis on such networks is powerful at uncovering critical knowledge hidden in large semi-structured datasets. Finally, we also present a few promising research directions.