File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Discovering meta-paths in large heterogeneous information networks

TitleDiscovering meta-paths in large heterogeneous information networks
Authors
Issue Date2015
PublisherInternational World Wide Web Conference Committee .
Citation
The 24th International World Wide Web Conference (WWW 2015), Florence, Italy, 18-22 May 2015. How to Cite?
AbstractThe 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.
Persistent Identifierhttp://hdl.handle.net/10722/208739
ISBN

 

DC FieldValueLanguage
dc.contributor.authorMeng, C-
dc.contributor.authorCheng, R-
dc.contributor.authorManiu, S-
dc.contributor.authorSenellart, p-
dc.contributor.authorZhang, W-
dc.date.accessioned2015-03-18T09:08:03Z-
dc.date.available2015-03-18T09:08:03Z-
dc.date.issued2015-
dc.identifier.citationThe 24th International World Wide Web Conference (WWW 2015), Florence, Italy, 18-22 May 2015.-
dc.identifier.isbn978-1-4503-3469-3-
dc.identifier.urihttp://hdl.handle.net/10722/208739-
dc.description.abstractThe 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.-
dc.languageeng-
dc.publisherInternational World Wide Web Conference Committee .-
dc.relation.ispartofInternational World Wide Web Conference, WWW 2015-
dc.titleDiscovering meta-paths in large heterogeneous information networks-
dc.typeConference_Paper-
dc.identifier.emailCheng, R: ckcheng@cs.hku.hk-
dc.identifier.emailManiu, S: smaniu@cs.hku.hk-
dc.identifier.emailZhang, W: wdzhang2@cs.hku.hk-
dc.identifier.authorityCheng, R=rp00074-
dc.identifier.doi10.1145/2736277.2741123-
dc.identifier.hkuros242617-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats