Network Representation Learning


Lab of Media and Network

Department of Computer Science & Technology

Tsinghua University

Asymmetric Transitivity Preserving Graph Embedding

M Ou, P Cui, Z Zhang, J Pei, W Zhu. SIGKDD 2016

In this paper, we aim to preserve asymmetric transitivity in directed graphs by approximating high-order proximities. We propose a scalable approximation algorithm, called High Order Proximity preserved Embedding (HOPE). In this algorithm, we first derive a general formulation of a class of high-order proximity measurements, then apply generalized SVD to the general formulation, whose time complexity is linear with the size of graph. The empirical study demonstrates the superiority of asymmetric transitivity and our proposed algorithm.


We provide the poster, pdf and ppt of this paper.



Links to datasets used in the paper:


Ou M, Cui P, Pei J, et al. Asymmetric transitivity preserving graph embedding[C] //Proc. of ACM SIGKDD. 2016: 1105-1114.