Scalable models for computing hierarchies in information networks
作者:Baoxu Shi, Tim Weninger
摘要
Information hierarchies are organizational structures that often used to organize and present large and complex information as well as provide a mechanism for effective human navigation. Fortunately, many statistical and computational models exist that automatically generate hierarchies; however, the existing approaches do not consider linkages in information networks that are increasingly common in real-world scenarios. Current approaches also tend to present topics as an abstract probably distribution over words, etc., rather than as tangible nodes from the original network. Furthermore, the statistical techniques present in many previous works are not yet capable of processing data at Web-scale. In this paper, we present the hierarchical document-topic model (HDTM), which uses a distributed vertex-programming process to calculate a nonparametric Bayesian generative model. Experiments on three medium- size data sets and the entire Wikipedia data set show that HDTM can infer accurate hierarchies even over large information networks.
论文关键词:Hierarchical clustering, Nonparametric Bayesian generative models, Vertex programming, Topic models, Model evaluation
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论文官网地址:https://doi.org/10.1007/s10115-016-0917-0