Predicting positive and negative links in signed social networks by transfer learning

By Jihang Ye, Hong Cheng, Zhe Zhu, Minghua Chen. 2013

150 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. Jiliang Tang
2. Xing Xie
3. Hongwei Wang
4. Charu C. Aggarwal
5. Fuzheng Zhang
6. Minyi Guo
7. Huan Liu
8. Chengqi Zhang

In Statistical and Nonlinear Physics

1. Charu C. Aggarwal
2. Jiliang Tang
3. Chengqi Zhang
4. Yi Chang
5. Huan Liu
6. Xingquan Zhu
7. Jie Yin
8. Aram Galstyan

Citations in the corpus

• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “ sentiment links. On the other hand, the complexity of sentiment generation and the sparsity of sentiment links make it hard for algorithms to achieve desirable performance. Recently, several studies [12, 14, 31, 35] propose methods to solve the problem of predicting signed links. However, they rely”
• A Survey of Signed Network Mining in Social Media (2015, methodology): “Us, Vs, Ut and Vt are four latent topological feature matrices [Ye et al. 2013].”
• Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks (2014, background): “Various approaches, including Bayesian inference [4], [5], multidimensional scaling (MDS) [6], matrix factorization [7], [8], [9], [10], [11], [12], and mixed-membership model [13], have been proposed to infer the static latent space representation of the observed network.”
• TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs (2015, methodology): “In this work, we also adopt the idea of graph transfer but focus on non-streaming scenarios for cross-network node classification.”
• Transfer Learning across Networks for Collective Classification (2013, background): “Another work [13] proposed an approach to leveraging the edge sign information across signed social networks for edg e sign prediction.”
• Negative Link Prediction in Social Media (2014, background): “In [27], the authors use the transfer learning approach to leverage sign information from an existing and mature signed network to predict signs for a newly formed signed social network.”