Link prediction by incidence matrix factorization

Sho Yokoi, Hiroshi Kajino, Hisashi Kashima

研究成果: Conference contribution

抄録

Link prediction suffers from the data sparsity problem. This paper presents and validates our hypothesis that, for sparse networks, incidence matrix factorization (IMF) could perform better than adjacency matrix factorization (AMF), which has been used in many previous studies. A key observation supporting the hypothesis is that IMF models a partially-observed graph more accurately than AMF. A technical challenge for validating our hypothesis is that, unlike AMF approach, there does not exist an obvious method to make predictions using a factorized incidence matrix. To this end, we newly develop an optimization-based link prediction method adopting IMF. We have conducted thorough experiments using synthetic and realworld datasets to investigate the relationship between the sparsity of a network and the performance of the aforementioned two methods. The experimental results show that IMF performs better than AMF as networks become sparser, which strongly validates our hypothesis.

本文言語English
ホスト出版物のタイトルFrontiers in Artificial Intelligence and Applications
編集者Gal A. Kaminka, Frank Dignum, Eyke Hullermeier, Paolo Bouquet, Virginia Dignum, Maria Fox, Frank van Harmelen
出版社IOS Press
ページ1730-1731
ページ数2
ISBN(電子版)9781614996712
DOI
出版ステータスPublished - 2016 1 1
イベント22nd European Conference on Artificial Intelligence, ECAI 2016 - The Hague, Netherlands
継続期間: 2016 8 292016 9 2

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
285
ISSN(印刷版)0922-6389

Conference

Conference22nd European Conference on Artificial Intelligence, ECAI 2016
CountryNetherlands
CityThe Hague
Period16/8/2916/9/2

ASJC Scopus subject areas

  • Artificial Intelligence

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