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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 3/2015

01.09.2015 | Regular Paper

Constructing topical hierarchies in heterogeneous information networks

verfasst von: Chi Wang, Jialu Liu, Nihit Desai, Marina Danilevsky, Jiawei Han

Erschienen in: Knowledge and Information Systems | Ausgabe 3/2015

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Abstract

Many digital documentary data collections (e.g., scientific publications, enterprise reports, news articles, and social media) can be modeled as a heterogeneous information network, linking text with multiple types of entities. Constructing high-quality hierarchies that can represent topics at multiple granularities benefits tasks such as search, information browsing, and pattern mining. In this work, we present an algorithm for recursively constructing multi-typed topical hierarchies. Contrary to traditional text-based topic modeling, our approach handles both textual phrases and multiple types of entities by a newly designed clustering and ranking algorithm for heterogeneous network data, as well as mining and ranking topical patterns of different types. Our experiments on datasets from two different domains demonstrate that our algorithm yields high-quality, multi-typed topical hierarchies.
Vorheriger Artikel Anonymizing graphs: measuring quality for clustering
Nächster Artikel The 10 million follower fallacy: audience size does not prove domain-influence on Twitter

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Fußnoten
1
We chose papers published in 20 conferences related to the areas of Artificial Intelligence, Databases, Data Mining, Information Retrieval, Machine Learning, and Natural Language Processing from http://​www.​dblp.​org/​.
 
2
As a paper is always published in exactly one venue, there can naturally be no venue–venue links.
 
3
The 16 topics chosen were: Bill Clinton, Boston Marathon, Earthquake, Egypt, Gaza, Iran, Israel, Joe Biden, Microsoft, Mitt Romney, Nuclear power, Steve Jobs, Sudan, Syria, Unemployment, US Crime.
 
4
The one exception is venues, as there are only 20 venues in the DBLP dataset, so we set \(K=3\) in this case.
 
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Metadaten
Titel
Constructing topical hierarchies in heterogeneous information networks
verfasst von
Chi Wang
Jialu Liu
Nihit Desai
Marina Danilevsky
Jiawei Han
Publikationsdatum
01.09.2015
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 3/2015
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-014-0777-4

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