Research Article
User Identity Linkage Across Social Networks Based on Neural Tensor Network
@INPROCEEDINGS{10.1007/978-3-030-66922-5_11, author={Xiaoyu Guo and Yan Liu and Xianmin Meng and Lian Liu}, title={User Identity Linkage Across Social Networks Based on Neural Tensor Network}, proceedings={Security and Privacy in New Computing Environments. Third EAI International Conference, SPNCE 2020, Lyngby, Denmark, August 6-7, 2020, Proceedings}, proceedings_a={SPNCE}, year={2021}, month={1}, keywords={User identity linkage Neural tensor network Network embedding Social network analysis}, doi={10.1007/978-3-030-66922-5_11} }- Xiaoyu Guo
Yan Liu
Xianmin Meng
Lian Liu
Year: 2021
User Identity Linkage Across Social Networks Based on Neural Tensor Network
SPNCE
Springer
DOI: 10.1007/978-3-030-66922-5_11
Abstract
User Identity Linkage (UIL) across social networks refers to the recognition of the accounts belonging to the same individual among multiple social network platforms. The most existing methods usually apply network embedding to map the network structure space to the low-dimensional vector space and then use linear models or standard neural network layers to measure the correlations between users across social networks. However, they can hardly model the complicated interactions between users. In this paper, we propose a novel Neural Tensor Network-based model for UIL, called NUIL. Firstly, we use the Random Walks and Skip-gram model to learn the vector representations of users. Then, we apply the Neural Tensor Network, which has a stronger ability to express the interactions between entities, to mine relationships between users from a higher dimension. A series of experiments conducted on a real-world dataset show that NUIL outperforms the state-of-the-art network structure-based methods in terms of precision, recall, and F1-measure, specifically the F1-measure exceeds 0.66, with an increase of more than 20%.
