Authors: Tong Man,Huawei Shen,Shenghua Liu,Xiaolong Jin,and Xueqi Cheng
Where published:
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) 2016 6
Document:
PDF
DOI
Abstract URL: http://www.bigdatalab.ac.cn/~shenhuawei/publications/2016/ijcai-man.pdf
Predicting anchor links across social networks has
important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation. One challenging problem is: whether and to what extent
we can address the anchor link prediction problem, if only structural information of networks is
available. Most existing methods, unsupervised or
supervised, directly work on networks themselves
rather than on their intrinsic structural regularities,
and thus their effectiveness is sensitive to the high
dimension and sparsity of networks. To offer a robust method, we propose a novel supervised model,
called PALE, which employs network embedding
with awareness of observed anchor links as supervised information to capture the major and specific structural regularities and further learns a stable cross-network mapping for predicting anchor
links. Through extensive experiments on two realistic datasets, we demonstrate that PALE significantly outperforms the state-of-the-art methods