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Link and interaction polarity predictions in signed networks

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Abstract

In today’s world, users typically take to online social media to express their opinions, which can inherently be both positive and negative. In fact, online social networks can be best modeled as signed networks, where opinions in the form of positive and negative links can exist between users, such as our friends and foes (e.g., “unfriended” users), respectively. Furthermore, users can also express their opinions to content generated by others through online social interactions, such as commenting or rating. Intuitively, these two types of opinions in the form of links and interactions should be related. For example, users’ interactions are likely to be positive (or negative) to those they have positively (or negatively) established links with. Similarly, we tend to establish positive (or negative) links with those whose generated content we frequently positively (or negatively) interact with online. Hence, in this paper, we first verify these assumptions by understanding the correlation between these two types of opinions from both a local and global perspective. Then, we propose a framework that jointly solves the link and interaction polarity prediction problem based on our newly found understanding of how these two problems are correlated. We ultimately perform experiments on a real-world signed network to demonstrate the effectiveness of our proposed approach to help mitigate both the data sparsity and cold-start problems found in the two tasks of link and interaction polarity prediction.

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Notes

  1. https://github.com/TylersNetwork/awesome-signed-network-datasets.

  2. https://github.com/TylersNetwork/link-interaction-polarity.

References

  • Abbasi F, Muzammal M, Qu Q (2018) A decentralized approach for negative link prediction in large graphs. In: 2018 IEEE international conference on data mining workshops (ICDMW). IEEE, pp 144–150

  • Agrawal P, Garg VK, Narayanam R (2013) Link label prediction in signed social networks. In: Proceedings of the twenty-third international joint conference on artificial intelligence, IJCAI ’13. AAAI Press, pp 2591–2597

  • Bhowmick AK, Meneni K, Mitra B (2019) On the network embedding in sparse signed networks. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 94–106

  • Cartwright D, Harary F (1956) Structural balance: a generalization of Heider’s theory. Psychol Rev 63(5):277

    Article  Google Scholar 

  • Cen Y, Gu R, Ji Y (2013) Sign inference for dynamic signed networks via dictionary learning. J Appl Math 2013(3):708581

    MATH  Google Scholar 

  • Chiang KY, Natarajan N, Tewari A, Dhillon IS (2011) Exploiting longer cycles for link prediction in signed networks. In: Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, pp 1157–1162

  • Chiang KY, Hsieh CJ, Natarajan N, Dhillon IS, Tewari A (2014) Prediction and clustering in signed networks: a local to global perspective. J Mach Learn Res 15(1):1177–1213

    MathSciNet  MATH  Google Scholar 

  • Derr T (2020) Network analysis with negative links. In: Proceedings of the 13th international conference on web search and data mining. ACM

  • Derr T, Tang J (2018) Congressional vote analysis using signed networks. In: 2018 IEEE international conference on data mining workshops (ICDMW). IEEE, pp 1501–1502

  • Derr T, Ma Yao, J Tang (2018a) Signed graph convolutional networks. In: 2018 IEEE international conference on data mining (ICDM). IEEE

  • Derr T, Aggarwal C, Tang J (2018b) Signed network modeling based on structural balance theory. In: Proceedings of the 2018 ACM on conference on information and knowledge management. ACM

  • Derr T, Wang C, Wang S, Tang J (2018c) Relevance measurements in online signed social networks. In: Proceedings of the 14th international workshop on mining and learning with graphs (MLG)

  • Derr T, Wang Z, Tang J (2018d) Opinions power opinions: joint link and interaction polarity predictions in signed networks. In: Proceedings of the 2018 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM ’18. ACM

  • Derr T, Johnson C, Chang Y, Tang J (2019) Balance in signed bipartite networks. In: Proceedings of the 28th ACM international conference on information and knowledge management. ACM, pp 1221–1230

  • Forsati R, Mahdavi M, Shamsfard M, Sarwat M (2014) Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Trans Inf Syst (TOIS) 32(4):17

    Article  Google Scholar 

  • Forsati R, Barjasteh I, Masrour F, Esfahanian AH, Radha H (2015) Pushtrust: an efficient recommendation algorithm by leveraging trust and distrust relations. In: Proceedings of the 9th ACM conference on recommender systems. ACM, pp 51–58

  • Guha R, Kumar R, Raghavan P, Tomkins A (2004) Propagation of trust and distrust. In: Proceedings of the 13th international conference on World Wide Web. ACM, pp 403–412

  • Hamilton WL, Ying R, Leskovec J (2017) Representation learning on graphs: methods and applications. arXiv preprint arXiv:170905584

  • Heider F (1946) Attitudes and cognitive organization. J Psychol 21(1):107–112

    Article  Google Scholar 

  • Hsieh CJ, Chiang KY, Dhillon IS (2012) Low rank modeling of signed networks. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 507–515

  • Huang J, Shen H, Hou L, Cheng X (2019) Signed graph attention networks. arXiv preprint arXiv:190610958

  • Islam MR, Prakash BA, Ramakrishnan N (2018) Signet: scalable embeddings for signed networks. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 157–169

  • Ito T, Shimbo M, Kudo T, Matsumoto Y (2005) Application of kernels to link analysis. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM, pp 586–592

  • Javari A, Jalili M (2014) Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links. ACM Trans Intell Syst Technol (TIST) 5(2):24

    Google Scholar 

  • Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907

  • Kunegis J, Lommatzsch A, Bauckhage C (2009) The slashdot zoo: mining a social network with negative edges. In: Proceedings of the 18th international conference on World wide web. ACM, pp 741–750

  • Kunegis J, Schmidt S, Lommatzsch A, Lerner J, De Luca EW, Albayrak S (2010) Spectral analysis of signed graphs for clustering, prediction and visualization. In: Proceedings of the 2010 SIAM international conference on data mining. SIAM, pp 559–570

  • Leskovec J, Huttenlocher D, Kleinberg J (2010a) Predicting positive and negative links in online social networks. In: Proceedings of the 19th international conference on World wide web. ACM, pp 641–650

  • Leskovec J, Huttenlocher D, Kleinberg J (2010b) Signed networks in social media. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1361–1370

  • Li L, Gu K, Zeng A, Fan Y, Di Z (2018) Modeling online social signed networks. Physica A 495:345–352

    Article  Google Scholar 

  • Lu C, Jiao P, Liu H, Wang Y, Xu H, Wang W (2019) SSNE: status signed network embedding. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 81–93

  • Ma H, Lyu MR, King I (2009) Learning to recommend with trust and distrust relationships. In: Proceedings of the third ACM conference on Recommender systems. ACM, pp 189–196

  • Moghaddam S, Jamali M, Ester M (2011) Review recommendation: personalized prediction of the quality of online reviews. In: Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, pp 2249–2252

  • Moghaddam S, Jamali M, Ester M (2012) Etf: extended tensor factorization model for personalizing prediction of review helpfulness. In: Proceedings of the fifth ACM international conference on Web search and data mining. ACM, pp 163–172

  • Sharma T (2012) Finding communities in weighted signed social networks. In: 2012 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 978–982

  • Shen P, Liu S, Wang Y, Han L (2019) Unsupervised negative link prediction in signed social networks. Math Probl Eng 2019:7348301

    Google Scholar 

  • Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:4

    Article  Google Scholar 

  • Symeonidis P, Mantas N (2013) Spectral clustering for link prediction in social networks with positive and negative links. Soc Netw Anal Min 3(4):1433–1447

    Article  Google Scholar 

  • Symeonidis P, Tiakas E (2014) Transitive node similarity: predicting and recommending links in signed social networks. World Wide Web 17(4):743–776

    Article  Google Scholar 

  • Tang J, Gao H, Hu X, Liu H (2013) Context-aware review helpfulness rating prediction. In: Proceedings of the 7th ACM conference on Recommender systems. ACM, pp 1–8

  • Tang J, Hu X, Liu H (2014) Is distrust the negation of trust? The value of distrust in social media. In: Proceedings of the 25th ACM conference on Hypertext and social media. ACM, pp 148–157

  • Tang J, Chang S, Aggarwal C, Liu H (2015) Negative link prediction in social media. In: Proceedings of the eighth ACM international conference on web search and data mining. ACM, pp 87–96

  • Tang J, Aggarwal C, Liu H (2016a) Node classification in signed social networks. In: Proceedings of the 2016 SIAM international conference on data mining. SIAM, pp 54–62

  • Tang J, Chang Y, Aggarwal C, Liu H (2016b) A survey of signed network mining in social media. ACM Comput Surv (CSUR) 49(3):42

    Article  Google Scholar 

  • Victor P, Cornelis C, De Cock M, Teredesai A (2009) Trust-and distrust-based recommendations for controversial reviews. In: Web Science Conference (WebSci’09: Society On-Line), 161

  • Victor P, Verbiest N, Cornelis C, Cock MD (2013) Enhancing the trust-based recommendation process with explicit distrust. ACM Trans Web (TWEB) 7(2):6

    Google Scholar 

  • Wan C, Fang Y, Wang C, Lv Y, Tian Z, Wang Y (2019) SignRank: a novel random walking based ranking algorithm in signed networks. Wirel Commun Mobile Comput 2019:4813717

    Google Scholar 

  • Wang S, Tang J, Liu H (2015) Toward dual roles of users in recommender systems. In: Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, pp 1651–1660

  • Wang S, Tang J, Aggarwal C, Chang Y, Liu H (2017) Signed network embedding in social media. In: Proceedings of the 2017 SIAM international conference on data mining. SIAM, pp 327–335

  • Wu Z, Aggarwal CC, Sun J (2016) The troll-trust model for ranking in signed networks. In: Proceedings of the Ninth ACM international conference on Web Search and Data Mining. ACM, pp 447–456

  • Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2019) A comprehensive survey on graph neural networks. arXiv preprint arXiv:190100596

  • Zhang D, Yin J, Zhu X, Zhang C (2018) Network representation learning: a survey. IEEE Trans Big Data 6(1):3–28

    Article  Google Scholar 

  • Zheng Q, Skillicorn DB (2015) Spectral embedding of signed networks. In: Proceedings of the 2015 SIAM international conference on data mining. SIAM, pp 55–63

  • Ziegler CN, Lausen G (2005) Propagation models for trust and distrust in social networks. Inf Syst Front 7(4–5):337–358

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank the anonymous reviewers for their helpful comments. Tyler Derr, Zhiwei Wang, Jamell Dacon and Jiliang Tang are supported by the National Science Foundation (NSF) under Grant Numbers IIS-1714741, IIS-1715940, IIS-1845081 and CNS-1815636, and a Grant from Criteo Faculty Research Award.

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  1. Data Science and Engineering Lab, Michigan State University, East Lansing, MI, USA

    Tyler Derr, Zhiwei Wang, Jamell Dacon & Jiliang Tang

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  1. Tyler Derr
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  2. Zhiwei Wang
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  4. Jiliang Tang
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Correspondence to Tyler Derr.

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Derr, T., Wang, Z., Dacon, J. et al. Link and interaction polarity predictions in signed networks. Soc. Netw. Anal. Min. 10, 18 (2020). https://doi.org/10.1007/s13278-020-0630-6

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