An algorithm for directed graph estimation

Hideitsu Hino, Atsushi Noda, Masami Tatsuno, Shotaro Akaho, Noboru Murata

研究成果: Conference contribution

抄録

A problem of estimating the intrinsic graph structure from observed data is considered. The observed data in this study is a matrix with elements representing dependency between nodes in the graph. Each element of the observed matrix represents, for example, co-occurrence of events at two nodes, or correlation of variables corresponding to two nodes. The dependency does not represent direct connections and includes influences of various paths, and spurious correlations make the estimation of direct connection difficult. To alleviate this difficulty, digraph Laplacian is used for characterizing a graph. A generative model of an observed matrix is proposed, and a parameter estimation algorithm for the model is also proposed. The proposed method is capable of dealing with directed graphs, while conventional graph structure estimation methods from an observed matrix are only applicable to undirected graphs. Experimental result shows that the proposed algorithm is able to identify the intrinsic graph structure.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings
出版社Springer Verlag
ページ145-152
ページ数8
ISBN(印刷版)9783319111780
DOI
出版ステータスPublished - 2014
イベント24th International Conference on Artificial Neural Networks, ICANN 2014 - Hamburg, Germany
継続期間: 2014 9 152014 9 19

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8681 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other24th International Conference on Artificial Neural Networks, ICANN 2014
CountryGermany
CityHamburg
Period14/9/1514/9/19

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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