Xiaoming Li
ABSTRACT
The purpose of this research is to design practical link prediction models in signed social networks. Current works focus on the sign prediction, based on the assumption that it is already known whether there is a link between any two users. In other words, the no-relation status is ignored. Meanwhile, the strength of existing links are assumed to be equal, which is also not realistic. In this study, we will redefine the link prediction problem in signed networks and take a deep investigation on no-relation status. Then, we aim to propose a personalized ranking model from the individual's perspective. This research explores link prediction models in a more realistic scenario, and it will contribute to ongoing research in development of link prediction and recommendations in signed networks. Furthermore, our research will provide a better understanding on the link formation mechanism behind signed network evolution.
- Lada A Adamic and Eytan Adar. 2003. Friends and neighbors on the web. Social networks Vol. 25, 3 (2003), 211--230.Google Scholar
- Tibor Antal, Paul L Krapivsky, and Sidney Redner. 2006. Social balance on networks: The dynamics of friendship and enmity. Physica D: Nonlinear Phenomena Vol. 224, 1 (2006), 130--136.Google ScholarCross Ref
- Lars Backstrom and Jure Leskovec. 2011. Supervised random walks: predicting and recommending links in social networks. In Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 635--644. Google ScholarDigital Library
- Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, and Inderjit S Dhillon. 2011. Exploiting longer cycles for link prediction in signed networks CIKM. ACM, 1157--1162. Google ScholarDigital Library
- James A Davis and Samuel Leinhardt. 1967. The structure of positive interpersonal relations in small groups. (1967).Google Scholar
- Armin Falk and Urs Fischbacher. 2006. A theory of reciprocity. Games and economic behavior Vol. 54, 2 (2006), 293--315.Google Scholar
- Hui Fang, Guibing Guo, and Jie Zhang. 2015. Multi-faceted trust and distrust prediction for recommender systems. Decision Support Systems Vol. 71 (2015), 37--47. Google ScholarDigital Library
- Marco Gori, Augusto Pucci, V Roma, and I Siena. 2007. ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines. IJCAI, Vol. Vol. 7. 2766--2771. Google ScholarDigital Library
- Cho-Jui Hsieh, Kai-Yang Chiang, and Inderjit S Dhillon. 2012. Low rank modeling of signed networks. In SIGKDD. ACM, 507--515. Google ScholarDigital Library
- Thorsten Joachims. 2002. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 133--142. Google ScholarDigital Library
- Jinhong Jung, Woojeong Jin, Lee Sael, and U Kang. 2016. Personalized ranking in signed networks using signed random walk with restart Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 973--978.Google Scholar
- Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Predicting positive and negative links in online social networks WWW. ACM, 641--650. Google ScholarDigital Library
- Xiaoming Li, Hui Fang, and Jie Zhang. 2017 a. A Feature-Based Approach for the Redefined Link Prediction Problem in Signed Networks. In International Conference on Advanced Data Mining and Applications. Springer, 165--179.Google ScholarCross Ref
- Xiaoming Li, Hui Fang, and Jie Zhang. 2017 b. Rethinking the Link Prediction Problem in Signed Social Networks. The Thirty-First AAAI Conference on Artificial Intelligence. 4955--4956.Google Scholar
- Xiaoming Li, Hui Fang, and Jie Zhang. 2018. FILE: A Novel Framework for Predicting Social Status in Signed Networks. The Thirty-Second AAAI Conference on Artificial Intelligence.Google Scholar
- David Liben-Nowell and Jon Kleinberg. 2007. The link-prediction problem for social networks. Journal of the American society for information science and technology Vol. 58, 7 (2007), 1019--1031. Google ScholarDigital Library
- Ryan N Lichtenwalter and Nitesh V Chawla. 2012. Vertex collocation profiles: subgraph counting for link analysis and prediction WWW. ACM, 1019--1028. Google ScholarDigital Library
- Ryan N Lichtenwalter, Jake T Lussier, and Nitesh V Chawla. 2010. New perspectives and methods in link prediction. In SIGKDD. ACM, 243--252. Google ScholarDigital Library
- Linyuan Lü and Tao Zhou. 2011. Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications Vol. 390, 6 (2011), 1150--1170.Google Scholar
- Mark EJ Newman. 2001. Clustering and preferential attachment in growing networks. Physical review E Vol. 64, 2 (2001), 025102.Google Scholar
- Moshen Shahriari and Mahdi Jalili. 2014. Ranking nodes in signed social networks. Social Network Analysis and Mining Vol. 4, 1 (2014), 172.Google ScholarCross Ref
- Donghyuk Shin, Si Si, and Inderjit S Dhillon. 2012. Multi-scale link prediction. In CIKM. ACM, 215--224. Google ScholarDigital Library
- Dongjin Song and David A Meyer. 2015. Recommending Positive Links in Signed Social Networks by Optimizing a Generalized AUC.. In AAAI. 290--296. Google ScholarDigital Library
- Panagiotis Symeonidis and Eleftherios Tiakas. 2014. Transitive node similarity: predicting and recommending links in signed social networks. World Wide Web Vol. 17, 4 (2014), 743--776. Google ScholarDigital Library
- Jiliang Tang, Yi Chang, Charu Aggarwal, and Huan Liu. 2016. A survey of signed network mining in social media. ACM Computing Surveys (CSUR) Vol. 49, 3 (2016), 42. Google ScholarDigital Library
- Zeynep Tufekci. 2010. Who Acquires Friends Through Social Media and Why?" Rich Get Richer" Versus" Seek and Ye Shall Find".. In ICWSM.Google Scholar
- Zhaoming Wu, Charu C Aggarwal, and Jimeng Sun. 2016. The troll-trust model for ranking in signed networks Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 447--456. Google ScholarDigital Library
- Jihang Ye, Hong Cheng, Zhe Zhu, and Minghua Chen. 2013. Predicting positive and negative links in signed social networks by transfer learning WWW. ACM, 1477--1488. Google ScholarDigital Library
- Tao Zhou, Linyuan Lü, and Yi-Cheng Zhang. 2009. Predicting missing links via local information. The European Physical Journal B-Condensed Matter and Complex Systems Vol. 71, 4 (2009), 623--630.Google ScholarCross Ref
- Tianchen Zhu, Zhaohui Peng, Xinghua Wang, and Xiaoguang Hong. 2017. Measuring the Similarity of Nodes in Signed Social Networks with Positive and Negative Links. In Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data. Springer, 399--407.Google Scholar
Index Terms
- Towards Practical Link Prediction Approaches in Signed Social Networks
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