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

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

SP - 145

EP - 152

BT - Artificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings

PB - Springer Verlag

T2 - 24th International Conference on Artificial Neural Networks, ICANN 2014

Y2 - 15 September 2014 through 19 September 2014

ER -