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A supervised learning approach to link prediction in Twitter

Abstract

The growth of social networks has lately attracted both academic and industrial researchers to study the ties between people, and how the social networks evolve with time. Social networks like Facebook, Twitter and Flickr require efficient and accurate methods to recommend friends to their users in the network. Several algorithms have been developed to recommend friends or predict likelihood of future links. Two main approaches are used to utilize those features; Score-based Approaches and Machine Learning Approaches. In a previous work, a score-based method was used based on topological, node and social features to calculate similarity between users and determine the likelihood of forming future links. This work has been extended by moving to a Machine Learning Approach which treats the prediction process as a classification problem. The classifier predicts the class of each edge whether it exists or doesn’t exist. Machine Learning Approaches have the benefit of adding all similarity indices needed as the feature set fed to the classifier. While in Score-based Approach when we used multiple features with associated weights, the performance was sensitive to the values of such weights. When machine learning is applied, the learning process is performed by the classifier which is fed by eight similarity indices representing connectivity, community, interaction and trust in social network. When indices are combined, a much higher accuracy than the previous Score-based Approach is obtained and hence enhancing the prediction accuracy. In order to evaluate the correctness of the proposed model, it has been applied on a real dataset of 2.974k users on the Twitter social network. Experiments show that using both classical and ensemble classifiers outperforms baseline algorithms when applied individually.

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Notes

  1. gephi.github.io/toolkit/.

References

  • Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230

    Article  Google Scholar 

  • Ahmed C, ElKorany A (2015) Enhancing link prediction in twitter using semantic user attributes. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining, pp 1155–1161, August 2015

  • Al Hasan M, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. In: SDM’06: workshop on link analysis, counter-terrorism and security, April 2006

  • Barbieri N, Bonchi F, Manco G (2014) Who to follow and why: link prediction with explanations. In: KDD’14

  • Bliss C, Frank M, Danforth C, Dodds P (2014) An evolutionary algorithm approach to link prediction in dynamic social networks. J Comput Sci 5:750–764

    Article  MathSciNet  Google Scholar 

  • Fire M, Tenenboim L, Lesser O, Puzis R, Rokach L, Elovici Y (2011) Link prediction in social networks using computationally efficient topological features. In: Privacy, security, risk and trust (PASSAT) and 2011 IEEE third international conference on social computing (SocialCom), pp 73–80

  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11:10–18

    Article  Google Scholar 

  • Han S, Xu Y (2016) Link prediction in microblog network using supervised learning with multiple features. J Comput 11(1):72–82

    Article  Google Scholar 

  • Hasan M, Zaki M (2011) A survey of link prediction in social networks. In: Aggarwal C (ed) Social network data analytics. Springer, Berlin

    Google Scholar 

  • Jang W, Kwak M (2014) A network link prediction model based on object-object match method. In: Proceedings of the southern association for information systems conference, Macon, GA, USA, March 21st–22nd, 2014

  • Jeh G, Widom J (2002) SimRank: a measure of structural-context similarity. In: Eighth ACM SIGKDD

  • Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18(1):39–43

    Article  MATH  Google Scholar 

  • Kim J, Choy M, Kim D, Kang U (2014) Link prediction based on generalized cluster information. In: Proceedings of the companion publication of the 23rd international conference on World Wide Web companion. International World Wide Web Conferences Steering Committee, pp 317–318

  • Li F, He J, Huang G, Zhang Y, Shi Y (2014) A clustering-based link prediction method in social networks. In: 14th international conference on computational science. ICCS 2014, pp 432–442

  • Liben-Nowell D, Kleinberg J (2003) The link prediction problem for social networks. In: Proceedings of CIKM’03. ACM Press, pp 556–559

  • Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inform Sci Technol 58:1019–1031

    Article  Google Scholar 

  • Lichtenwalter RN, Lussier JT, Chawla NV (2010) New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, ACM

  • Newman M (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64:025102

    Article  Google Scholar 

  • Papadimitriou A, Symeonidis P, Manolopoulos Y (2012) Fast and accurate link prediction in social networking systems. J Syst Softw 85:2119–2132

    Article  Google Scholar 

  • Pavlov M, Ichise R (2007) Finding experts by link prediction in co-authorship networks. FEWS 290:42–55

    Google Scholar 

  • Rowe M, Stankovic M, Alani H (2012) Who will follow whom? Exploiting semantics for link prediction in attention-information networks. In: Proceedings of the 11th international conference on the semantic web—volume part I (ISWC’12)

  • Sá HR, Prudêncio RB (2010) Supervised learning for link prediction in weighted networks. In: III international workshop on web and text intelligence

  • Salton G, McGill M (1986) Introduction to modern information retrieval. McGraw-Hill, New York City

    MATH  Google Scholar 

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

    Article  Google Scholar 

  • Symeonidis P, Tiakas E, Manolopoulos Y (2010) Transitive node similarity for link prediction in social networks with positive and negative links. In: RecSys

  • Valverde-Rebaza J, de Andrade Lopes A (2012a) Link prediction in complex networks based on cluster information. In: Advances in artificial intelligence, SBIA

  • Valverde-Rebaza J, de Andrade Lopes A (2012b) Structural link prediction using community information on twitter. In: Computational aspects of social networks (CASoN), 2012 fourth international conference. IEEE, pp 132–137

  • Valverde-Rebaza J, de Andrade Lopes A (2013) Exploiting behaviors of communities of twitter users for link prediction. Soc Netw Anal Min 3(4):1063–1074

    Article  Google Scholar 

  • Xiang E (2008) A survey on link prediction models for networked data. Department of Computer Science and Engineering, HKUST, Kowloon

    Google Scholar 

  • Volkova S, Hsu WH Link prediction in social networks. Independent Project

  • Yantao J, Yuanzhuo W, Jingyuan L, Kai F, Xueqi C, Jianchen L (2013) Structural-interaction link prediction in microblogs. In: Proceedings of the 22nd international conference on World Wide Web companion. International World Wide Web Conferences Steering Committee, pp 193–194

  • Yin D, Hong L, Xiong X, Davison B (2011) Link formation analysis in microblogs. In: ACM SIGIR

  • Zheleva E, Getoor L, Golbeck J, Kuter U (2010) Using friendship ties and family circles for link prediction. In: Giles L, Smith M, Yen J, Zhang H (eds) Advances in social network mining and analysis. Springer, Berlin, pp 97–113

    Chapter  Google Scholar 

  • Zhu L, Lerman K (2014) A visibility-based model for link prediction in social media. In: ASE BIGDATA/SOCIALCOM/CYBERSECURITY conference

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Correspondence to Cherry Ahmed.

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Ahmed, C., ElKorany, A. & Bahgat, R. A supervised learning approach to link prediction in Twitter. Soc. Netw. Anal. Min. 6, 24 (2016). https://doi.org/10.1007/s13278-016-0333-1

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