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Are we friends or enemies?: Let's ask thy neighbour!

Published: 17 March 2020 Publication History

Abstract

With the richness of interactions among users emerging through different social media applications, drawing conclusive evidence about the sign of these relations (positive and negative) is receiving significant attention. In this paper, we propose an adaptive link prediction system which tactfully ensembles both local and nonlocal attributes of an edge to predict it's sign while considering the high variance of the network and handling the inherent sparsity of the graph. Experimental validation on signed networks, like Slash-dot and Epinions indicate the proposed approach can ensure high significant prediction accuracy when compared with the existing research works.

References

[1]
Xiao Chen, Jing-Feng Guo, Xiao Pan, and Chunying Zhang. 2017. Link prediction in signed networks based on connection degree. Journal of Ambient Intelligence and Humanized Computing (2017), 1--11.
[2]
Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, and Inderjit S Dhillon. 2011. Exploiting longer cycles for link prediction in signed networks. In Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, 1157--1162.
[3]
Amin Javari and Mahdi Jalili. 2014. Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links. ACM Transactions on Intelligent Systems and Technology (TIST) 5, 2 (2014), 24.
[4]
Amin Javari, HongXiang Qiu, Elham Barzegaran, Mahdi Jalili, and Kevin Chen-Chuan Chang. 2017. Statistical Link Label Modeling for Sign Prediction: Smoothing Sparsity by Joining Local and Global Information. In Data Mining (ICDM), 2017 IEEE International Conference on. IEEE, 1039--1044.
[5]
Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proceedings of the 19th international conference on World wide web. ACM, 641--650.
[6]
Jiliang Tang, Yi Chang, Charu Aggarwal, and Huan Liu. 2016. A survey of signed network mining in social media. ACM Computing Surveys (CSUR) 49, 3 (2016), 42.
[7]
Linchuan Xu, Xiaokai Wei, Jiannong Cao, and S Yu Philip. 2017. Disentangled Link Prediction for Signed Social Networks via Disentangled Representation Learning. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 676--685.
[8]
Weiwei Yuan, Kangya He, Donghai Guan, and Guangjie Han. 2017. Edge-dual graph preserving sign prediction for signed social networks. IEEE Access 5 (2017), 19383--19392.

Cited By

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  • (2024)Sign prediction based on node connecting tightness in complex networkInternational Journal of Modern Physics C10.1142/S0129183124502139Online publication date: 7-Sep-2024
  • (2023)SigGAN: Adversarial Model for Learning Signed Relationships in NetworksACM Transactions on Knowledge Discovery from Data10.1145/353261017:1(1-20)Online publication date: 20-Feb-2023

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cover image ACM Conferences
IUI '20 Companion: Companion Proceedings of the 25th International Conference on Intelligent User Interfaces
March 2020
153 pages
ISBN:9781450375139
DOI:10.1145/3379336
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 17 March 2020

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Cited By

View all
  • (2024)Sign prediction based on node connecting tightness in complex networkInternational Journal of Modern Physics C10.1142/S0129183124502139Online publication date: 7-Sep-2024
  • (2023)SigGAN: Adversarial Model for Learning Signed Relationships in NetworksACM Transactions on Knowledge Discovery from Data10.1145/353261017:1(1-20)Online publication date: 20-Feb-2023

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