Negative Link Prediction in Social Media

By Jiliang Tang, Shiyu Chang, Charu C. Aggarwal, Huan Liu. 2014

147 citations recorded by the source corpus.

Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework.

Subfields: Artificial Intelligence, Information Systems, 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. Fuzheng Zhang
5. Minyi Guo
6. X. Shen
7. Fu-lai Chung
8. Charu C. Aggarwal

In Information Systems

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

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. Suhang Wang
8. Xing Xie

Citations in the corpus

• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “ral balance theory. Ye et al. [31] utilize transfer learning to leverage edge sign information from source network and improve prediction accuracy in target network. Tang et al. design NeLP framework [21] which exploits positive links in social media to predict negative links. The difference between”
• A Survey of Signed Network Mining in Social Media (2015, methodology): “Examples of signed networks in social media include trust/distrust in Epinions1 [Massa and Avesani 2005; Leskovec et al. 2010a] and friends/foes in Slashdot2 [Kunegis et al. 2009].”
• Deep Network Embedding for Graph Representation Learning in Signed Networks (2019, methodology): “Then, we apply vector-based machine learning algorithms on the node vector representations learned by DNE-SBP, to carry out two important signed network mining tasks, namely, link sign prediction [29]–[31] and community detection [13], [32]–[34].”
• Recommender Systems (2016, unknown): “The interaction between trust and distrust concepts is studied in [590, 591] in the context of the recommendation and link prediction problems.”
• Signed Network Embedding in Social Media (2017, background): “However, the aforementioned algorithms are designed for unsigned network and don't take negative links into consideration, while negative links have been proven to have distinct properties and added value over positive links [10] [20].”