### 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.

元の言語 | English |
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ホスト出版物のタイトル | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

出版者 | Springer Verlag |

ページ | 145-152 |

ページ数 | 8 |

巻 | 8681 LNCS |

ISBN（印刷物） | 9783319111780 |

DOI | |

出版物ステータス | Published - 2014 |

イベント | 24th International Conference on Artificial Neural Networks, ICANN 2014 - Hamburg, Germany 継続期間: 2014 9 15 → 2014 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) |
---|---|

巻 | 8681 LNCS |

ISSN（印刷物） | 03029743 |

ISSN（電子版） | 16113349 |

### Other

Other | 24th International Conference on Artificial Neural Networks, ICANN 2014 |
---|---|

国 | Germany |

市 | Hamburg |

期間 | 14/9/15 → 14/9/19 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### これを引用

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(巻 8681 LNCS, pp. 145-152). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 8681 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_19

**An algorithm for directed graph estimation.** / Hino, Hideitsu; Noda, Atsushi; Tatsuno, Masami; Akaho, Shotaro; Murata, Noboru.

研究成果: Conference contribution

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*巻. 8681 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 巻. 8681 LNCS, Springer Verlag, pp. 145-152, 24th International Conference on Artificial Neural Networks, ICANN 2014, Hamburg, Germany, 14/9/15. https://doi.org/10.1007/978-3-319-11179-7_19

}

TY - GEN

T1 - An algorithm for directed graph estimation

AU - Hino, Hideitsu

AU - Noda, Atsushi

AU - Tatsuno, Masami

AU - Akaho, Shotaro

AU - Murata, Noboru

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - digraph Laplacian

KW - directed graph

KW - graph estimation

UR - http://www.scopus.com/inward/record.url?scp=84958542694&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958542694&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-11179-7_19

DO - 10.1007/978-3-319-11179-7_19

M3 - Conference contribution

AN - SCOPUS:84958542694

SN - 9783319111780

VL - 8681 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 145

EP - 152

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -