NDLI: Discovering Meta-Paths in Large Heterogeneous Information Networks

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Discovering Meta-Paths in Large Heterogeneous Information Networks

Content Provider	ACM Digital Library
Author	Maniu, Silviu Senellart, Pierre Cheng, Reynold Meng, Changping Zhang, Wangda
Abstract	The Heterogeneous Information Network (HIN) is a graph data model in which nodes and edges are annotated with class and relationship labels. Large and complex datasets, such as Yago or DBLP, can be modeled as HINs. Recent work has studied how to make use of these rich information sources. In particular, meta-paths, which represent sequences of node classes and edge types between two nodes in a HIN, have been proposed for such tasks as information retrieval, decision making, and product recommendation. Current methods assume meta-paths are found by domain experts. However, in a large and complex HIN, retrieving meta-paths manually can be tedious and difficult. We thus study how to discover meta-paths automatically. Specifically, users are asked to provide example pairs of nodes that exhibit high proximity. We then investigate how to generate meta-paths that can best explain the relationship between these node pairs. Since this problem is computationally intractable, we propose a greedy algorithm to select the most relevant meta-paths. We also present a data structure to enable efficient execution of this algorithm. We further incorporate hierarchical relationships among node classes in our solutions. Extensive experiments on real-world HIN show that our approach captures important meta-paths in an efficient and scalable manner.
Starting Page	754
Ending Page	764
Page Count	11
File Format	PDF
ISBN	9781450334693
DOI	10.1145/2736277.2741123
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Knowledge bases Heterogeneous networks Meta paths
Content Type	Text
Resource Type	Article

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