Spectral Embedding of Signed Networks

By Quan Zheng, David B. Skillicorn. 2015

52 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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. Xing Xie
2. Jiliang Tang
3. Hongwei Wang
4. Fuzheng Zhang
5. Minyi Guo
6. X. Shen
7. Fu-lai Chung
8. Charu C. Aggarwal

In Statistical and Nonlinear Physics

1. X. Shen
2. Fu-lai Chung
3. Charu C. Aggarwal
4. Jiliang Tang
5. Yi Chang
6. Huan Liu
7. Xing Xie
8. Minyi Guo

Citations in the corpus

• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “Recently, several studies [12, 14, 31, 35] propose methods to solve the problem of predicting signed links.”
• A Survey of Signed Network Mining in Social Media (2015, methodology): “Hence a balanced normalized signed Laplacian matrix is proposed in [Zheng and Skillicorn 2015] as: L = (Dp −Ap +An) (18) Another spectral clustering technique is balanced normalized cut [Chiang et al. 2012].”
• Deep Network Embedding for Graph Representation Learning in Signed Networks (2019, methodology): “” Existing signed network embedding algorithms mostly employ the spectral techniques [7], [17]–[19] to embed the original network into a low-dimensional space spanned by”