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 -