Predict Anchor Links across Social Networks via an Embedding Approach

By Tong Man, Huawei Shen, Shenghua Liu, Xiaolong Jin, Xueqi Cheng. 2016

263 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xiao Wang
2. Vincent Wenchen Zheng
3. Yuxiao Dong
4. Xiao Liu
5. Peng Cui
6. Hongyun Cai
7. Shirui Pan
8. Kevin Chen-Chuan Chang

Citations in the corpus

• A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “Both [26] and [18] learn node embedding to align users across different social networks, i.”
• A Survey on Network Embedding (2017, background): “[22] extend the original sparse networks G and G to the denser networks.”
• Cross-Domain Recommendation: An Embedding and Mapping Approach (2017, unknown): “At the first step, we aim to find the representation of users/items in the latent space by latent factor models [Koren et al., 2009; Man et al., 2016].”
• OAG: Toward Linking Large-scale Heterogeneous Entity Graphs (2019, background): “Recently, network embedding based approaches [9, 17, 18] have been proposed to address the user alignment problem across social networks.”