A Survey of Signed Network Mining in Social Media

By Jiliang Tang, Yi Chang, Charu C. Aggarwal, Huan Liu. 2015

358 citations recorded by the source corpus.

Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines. With the increasing prevalence of social media networks, signed network analysis has evolved from developing and measuring theories to mining tasks. In this article, we present a review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers. We begin by giving basic concepts and unique properties and principles of signed networks. Then we classify and review tasks of signed network mining with representative algorithms. We also delineate some tasks that have not been extensively studied with formal definitions and also propose research directions to expand the field of signed network mining.

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

In Statistical and Nonlinear Physics

1. Charu C. Aggarwal
2. Huan Liu
3. Jiliang Tang
4. X. Shen
5. Fu-lai Chung
6. Yi Chang
7. Srijan Kumar
8. Yao Ma

Citations in the corpus

• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (2017, background): “Our problem of predicting positive and negative sentiment links connects to a large body of work on signed social networks, including trust propagation [9], spectral analysis [13], and social media mining [22].”
• Deep Network Embedding for Graph Representation Learning in Signed Networks (2019, methodology): “Since the structural balance theory is naturally defined for undirected networks [8], we evaluated all the comparing algorithms in undirected signed networks.”
• Signed Network Embedding in Social Media (2017, background): “The availability of negative links in signed networks challenges some principles that explain the formation and properties of links for unsigned social networks; and principles for signed social networks can be substantially different from that of unsigned network [7] [8].”
• SNE: Signed Network Embedding (2017, background): “Thus, they cannot simply migrate to signed networks because the negative links change the theories or assumptions on which unsigned network embedding models rely [28].”
• DEBAGREEMENT: A comment-reply dataset for (dis)agreement detection in online debates (2021, methodology): “Modeling online discussion forums as graphs of interactions between users enables researchers to: i) translate the (dis)agreement detection task into a sign link prediction one [45], ii) use recent advances in GRL methods [20], and iii) leverage existing signed graph embedding methods [41, 15, 30] t”
• Signed Graph Convolutional Networks (2018, background): “While some problems such as link sign prediction [18], [21] and centrality [22] have been heavily explored, other directions such as network modeling [23], node relevance [24], and network embedding [19] are still in early development [25].”
• Edge Weight Prediction in Weighted Signed Networks (2016, background): “Therefore, we define how to assign fairness and goodness scores to each vertex in this section.”