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Article: Spectral Clustering in Heterogeneous Information Networks
Title | Spectral Clustering in Heterogeneous Information Networks |
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Authors | |
Issue Date | 2019 |
Publisher | AAAI Press. The Journal's web site is located at https://aaai.org/Library/AAAI/aaai-library.php |
Citation | Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu,Hawaii, USA, 27 January – 1 February 2019, v. 33 n. 1, p. 4221-4228 How to Cite? |
Abstract | A heterogeneous information network (HIN) is one whose objects are of different types and links between objects could model different object relations. We study how spectral clustering can be effectively applied to HINs. In particular, we focus on how meta-path relations are used to construct an effective similarity matrix based on which spectral clustering is done. We formulate the similarity matrix construction as an optimization problem and propose the SClump algorithm for solving the problem. We conduct extensive experiments comparing SClump with other state-of-the-art clustering algorithms on HINs. Our results show that SClump outperforms the competitors over a range of datasets w.r.t. different clustering quality measures. |
Description | Section: AAAI Technical Track: Machine Learning Open Access Journal |
Persistent Identifier | http://hdl.handle.net/10722/277269 |
ISBN | |
ISSN |
DC Field | Value | Language |
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dc.contributor.author | Li, X | - |
dc.contributor.author | Kao, CM | - |
dc.contributor.author | Ren, Z | - |
dc.contributor.author | Yin, D | - |
dc.date.accessioned | 2019-09-20T08:47:50Z | - |
dc.date.available | 2019-09-20T08:47:50Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu,Hawaii, USA, 27 January – 1 February 2019, v. 33 n. 1, p. 4221-4228 | - |
dc.identifier.isbn | 978-1-57735-809-1 | - |
dc.identifier.issn | 2159-5399 | - |
dc.identifier.uri | http://hdl.handle.net/10722/277269 | - |
dc.description | Section: AAAI Technical Track: Machine Learning | - |
dc.description | Open Access Journal | - |
dc.description.abstract | A heterogeneous information network (HIN) is one whose objects are of different types and links between objects could model different object relations. We study how spectral clustering can be effectively applied to HINs. In particular, we focus on how meta-path relations are used to construct an effective similarity matrix based on which spectral clustering is done. We formulate the similarity matrix construction as an optimization problem and propose the SClump algorithm for solving the problem. We conduct extensive experiments comparing SClump with other state-of-the-art clustering algorithms on HINs. Our results show that SClump outperforms the competitors over a range of datasets w.r.t. different clustering quality measures. | - |
dc.language | eng | - |
dc.publisher | AAAI Press. The Journal's web site is located at https://aaai.org/Library/AAAI/aaai-library.php | - |
dc.relation.ispartof | Proceedings of the AAAI Conference on Artificial Intelligence | - |
dc.rights | Copyright (c) 2019 Association for the Advancement of Artificial Intelligence | - |
dc.title | Spectral Clustering in Heterogeneous Information Networks | - |
dc.type | Article | - |
dc.identifier.email | Kao, CM: kao@cs.hku.hk | - |
dc.identifier.authority | Kao, CM=rp00123 | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1609/aaai.v33i01.33014221 | - |
dc.identifier.hkuros | 305602 | - |
dc.identifier.volume | 33 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 4221 | - |
dc.identifier.epage | 4228 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2159-5399 | - |