Directed graph embedding

Authors:
Mo Chen;Qiong Yang;Xiaoou Tang
Affiliations:
Tsinghua University, Beijing, P.R.China;Microsoft Research Asia, Beijing, P.R.China;Microsoft Research Asia, Beijing, P.R.China
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

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Abstract

In this paper, we propose the Directed Graph Embedding (DGE) method that embeds vertices on a directed graph into a vector space by considering the link structure of graphs. The basic idea is to preserve the locality property of vertices on a directed graph in the embedded space. We use the transition probability together with the stationary distribution of Markov random walks to measure such locality property. It turns out that by exploring the directed links of the graph using random walks, we can get an optimal embedding on the vector space that preserves the local affinity which is inherent in the directed graph. Experiments on both synthetic data and real-world Web page data are considered. The application of our method to Web page classification problems gets a significant improvement comparing with state-of-art methods.