Inferring anchor links across multiple heterogeneous social networks

By Xiangnan Kong, Jiawei Zhang, Philip S. Yu. International Conference on Information and Knowledge Management, 2013

368 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/61745e0a984e8fd9c45f545c3c83f7b99b110505

How researchers see this paper

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

In Artificial Intelligence

1. Jie Tang
2. Philip S. Yu
3. Jian Pei
4. Zhilin Yang
5. Xiaojun Chang
6. Xueqi Cheng
7. Huawei Shen
8. Po-Yao (Bernie) Huang

In Statistical and Nonlinear Physics

1. Jie Tang
2. Jian Pei
3. Philip S. Yu
4. Zhilin Yang
5. Xueqi Cheng
6. Huawei Shen
7. Xiao Wang
8. Chuan Shi

Citations in the corpus

• Predict Anchor Links across Social Networks via an Embedding Approach (2016, background): “For supervised methods, namely, MNA, CRW, and PALE, to evaluate their performance, we conduct 10 runs of the exper-iment with sampling ↵ t = 3% anchor links for training.”
• COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency (2015, methodology): “MNA [16]: This method leverages the output score of SVM as the initial pairwise score and then optimize the matching problem by satisfying the one-to-one constraints.”
• A Survey of Deep Active Learning (2020, methodology): “011 Robotics [83, 96] HHAR [214], NWFD [145] Smart device [37] Foursquare, Twitter [116] Social network”