An algorithm for directed graph estimation

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319111780
Publication statusPublished - 2014
Event24th International Conference on Artificial Neural Networks, ICANN 2014 - Hamburg, Germany
Duration: 2014 Sept 152014 Sept 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8681 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other24th International Conference on Artificial Neural Networks, ICANN 2014


  • digraph Laplacian
  • directed graph
  • graph estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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