A Hierarchical Factor Model for Directed Graph Data

• Main Authors: Chen, Chang-Ju, 陳昶汝
• Other Authors: Lu, Horng-Shing
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/67816990807228139067

碩士 === 國立交通大學 === 統計學研究所 === 104 === Nowadays, graph theory has become a key role towards analyzing pairwise relations between objects. It is commonly and widely applied to describe complex interaction patterns in various scientific fields. Most of the researches explore community structure only. However, recent studies unveil more sophisticated modules; community is not the only structure we are interested in anymore. In this paper, we extend the previous undirected factor model and put forward a directed graph factor model, where every node carries several features, for analyzing various complex network data. Link functions map the features of each pair nodes to every one-direction edge probabilities, and a factor which can be determined by a specific kind of link function refers a channel for the one-way edge connection. This model naturally incorporates different kinds of link functions which yield distinct types of modules. DIC is used for our model selection, k-mean is for clustering, and MCMC procedures is for the inference of the model. We successfully analyze the structure of every sub-circle and the members in each sub-circle. In the future, we may choose different assumptions to improve the accurate rate, find other methods to improve the computation efficiency and to set better initial values.