Measuring the Similarity of Nodes in Signed Social Networks with Positive and Negative Links

  • Tianchen Zhu
  • Zhaohui Peng
  • Xinghua Wang
  • Xiaoguang Hong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10366)

Abstract

Similarity measure in non-signed social networks has been extensively studied for decades. However, how to measure the similarity of two nodes in signed social networks remains an open problem. It is challenging to incorporate both positive and negative relationships simultaneously in signed social networks due to the opposite opinions implied by them. In this paper, we study the similarity measure problem in signed social networks. We propose a basic node similarity measure that can utilize both positive and negative relations in signed social networks by comparing the immediate neighbors of two objects. Moreover, we exploit the propagation of similarity in networks. Finally, we perform extensive experimental comparison of the proposed method against existing algorithms on real data set. Our experimental results show that our method outperforms other approaches.

Keywords

Similarity measure Signed networks Positive and negative links 

Notes

Acknowledgement

This work is supported by NSF of China (No. 61602237), 973 Program (No. 2015CB352501), NSF of Shandong, China (No. ZR2013FQ009), the Science and Technology Development Plan of Shandong, China (Nos. 2014GGX101047, 2014GGX101019).

References

  1. 1.
    Kunegis, J., Lommatzsch, A., Bauckhage, C.: The Slashdot Zoo: mining a social network with negative edges. In: WWW 2009, pp. 741–750 (2009)Google Scholar
  2. 2.
    Leskovec, J., Huttenlocher, D.P., Kleinberg, J.M.: Predicting positive and negative links in online social networks. In: WWW 2010, pp. 641–650 (2010)Google Scholar
  3. 3.
    Tang, J., Chang, Y., Aggarwal, C., Liu, H.: A survey of signed network mining in social media. ACM Comput. Surv. 49(3), 1–37 (2016)CrossRefGoogle Scholar
  4. 4.
    Wang, S., Hu, X., Yu, P.S., Li, Z.: MMRate: inferring multi-aspect diffusion networks with multi-pattern cascades. In: KDD 2014, pp. 1246–1255 (2014)Google Scholar
  5. 5.
    Wang, S., Yan, Z., Hu, X., Yu, P.S., Li, Z.: Burst time prediction in cascades. In: AAAI 2015, pp. 325–331 (2015)Google Scholar
  6. 6.
    Shi, C., Li, Y., Zhang, J., Sun, Y., Yu, P.S.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2017)CrossRefGoogle Scholar
  7. 7.
    Pan, J., Yang, H., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: KDD 2004, pp. 653–658 (2004)Google Scholar
  8. 8.
    Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: KDD 2002, pp. 538–543 (2002)Google Scholar
  9. 9.
    Jeh, G., Widom, J.: Scaling personalized web search. In: WWW 2003, pp. 271–279 (2003)Google Scholar
  10. 10.
    Liben-Nowell, D., Kleinberg, J.M.: The link prediction problem for social networks. In: CIKM 2003, pp. 556–559 (2003)Google Scholar
  11. 11.
    Symeonidis, P., Tiakas, E., Manolopoulos, Y.: Transitive node similarity for link prediction in social networks with positive and negative links. In: RecSys 2010, pp. 183–190 (2010)Google Scholar
  12. 12.
    Symeonidis, P., Tiakas, E.: Transitive node similarity: predicting and recommending links in signed social networks. World Wide Web 17(4), 743–776 (2014)CrossRefGoogle Scholar
  13. 13.
    Heider, F.: Attitudes and cognitive organization. J. Psychol. 21, 107–112 (1946)CrossRefGoogle Scholar
  14. 14.
    Cartwright, D., Harary, F.: structure balance: a generalization of Heider’s theory. Psychol. Rev. 63(5), 277–293 (1956)CrossRefGoogle Scholar
  15. 15.
    Leskovec, J., Huttenlocher, D.P., Kleinberg, J.M.: Signed networks in social media. In: CHI 2010, pp. 1361–1370 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Tianchen Zhu
    • 1
  • Zhaohui Peng
    • 1
  • Xinghua Wang
    • 1
  • Xiaoguang Hong
    • 1
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina

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