Link Prediction in Multi-modal Social Networks

  • Panagiotis Symeonidis
  • Christos Perentis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8726)


Online social networks like Facebook recommend new friends to users based on an explicit social network that users build by adding each other as friends. The majority of earlier work in link prediction infers new interactions between users by mainly focusing on a single network type. However, users also form several implicit social networks through their daily interactions like commenting on people’s posts or rating similarly the same products. Prior work primarily exploited both explicit and implicit social networks to tackle the group/item recommendation problem that recommends to users groups to join or items to buy. In this paper, we show that auxiliary information from the user-item network fruitfully combines with the friendship network to enhance friend recommendations. We transform the well-known Katz algorithm to utilize a multi-modal network and provide friend recommendations. We experimentally show that the proposed method is more accurate in recommending friends when compared with two single source path-based algorithms using both synthetic and real data sets.


link prediction friend recommendation 


  1. 1.
    Adamic, L., Adar, E.: Friends and neighbors on the web. Social Networks 25(3), 211–230 (2003)CrossRefGoogle Scholar
  2. 2.
    Adamic, L., Adar, E.: How to search a social network. Social Networks 27(3), 187–203 (2005)CrossRefGoogle Scholar
  3. 3.
    Cha, M., Mislove, A., Gummadi, K.: A measurement-driven analysis of information propagation in the flickr social network. In: 18th International World Wide Web Conference, pp. 721–730 (2009)Google Scholar
  4. 4.
    Chen, J., Geyer, W., Dugan, C., Muller, M., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: 27th International Conference on Human Factors in Computing Systems, pp. 201–210 (2009)Google Scholar
  5. 5.
    Davis, D., Lichtenwalter, R., Chawla, N.V.: Multi-relational link prediction in heterogeneous information networks. In: IEEE International Conference Advances in Social Networks Analysis and Mining, pp. 281–288 (2011)Google Scholar
  6. 6.
    Du, N., Faloutsos, C., Wang, B., Akoglu, L.: Large human communication networks: Patterns and a utility-driven generator. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 269–278 (2009)Google Scholar
  7. 7.
    Du, N., Wang, H., Faloutsos, C.: Analysis of large multi-modal social networks: Patterns and a generator. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part I. LNCS, vol. 6321, pp. 393–408. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Foster, K., Muth, S., Potterat, J., Rothenberg, R.: A faster katz status score algorithm. Computational & Mathematical Organization Theory 7(4), 275–285 (2001)CrossRefGoogle Scholar
  9. 9.
    Golbeck, J.: Personalizing applications through integration of inferred trust values in semantic web-based social networks. In: Semantic Network Analysis Workshop at the 4th International Semantic Web Conference, vol. 16, p. 30 (2005)Google Scholar
  10. 10.
    Guy, I., Ronen, I., Wilcox, E.: Do you know?: Recommending people to invite into your social network. In: 14th ACM International Conference on Intelligent User Interfaces, pp. 77–86 (2009)Google Scholar
  11. 11.
    Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)CrossRefzbMATHGoogle Scholar
  12. 12.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. In: Conference on Information and Knowledge Management, pp. 556–559 (2003)Google Scholar
  13. 13.
    Lo, S., Lin, C.: Wmr: a graph-based algorithm for friend recommendation. In: IEEE/ACM International Conference on Web Intelligence, pp. 121–128 (2006)Google Scholar
  14. 14.
    Lü, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  15. 15.
    Lu, Z., Savas, B., Tang, W., Dhillon, I.S.: Supervised link prediction using multiple sources. In: 10th International Conference on Data Mining, pp. 923–928 (2010)Google Scholar
  16. 16.
    Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: Meersman, R. (ed.) OTM 2004. LNCS, vol. 3290, pp. 492–508. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Milgram, S.: The small world problem. Psychology Today 22, 61–67 (1967)Google Scholar
  18. 18.
    Pan, J., Yang, H., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 653–658 (2004)Google Scholar
  19. 19.
    Scholz, C., Atzmueller, M., Barrat, A., Cattuto, C., Stumme, G.: New insights and methods for predicting face-to-face contacts. In: 7th International AAAI Conference on Weblogs and Social Media, pp. 281–288 (2013)Google Scholar
  20. 20.
    Symeonidis, P., Tiakas, E., Manolopoulos, Y.: Transitive node similarity for link prediction in social networks with positive and negative links. In: 4th ACM Conference on Recommender systems, pp. 183–190 (2010)Google Scholar
  21. 21.
    Tong, H., Faloutsos, C., Pan, J.Y.: Fast random walk with restart and its applications. In: 6th International Conference on Data Mining, pp. 613–622 (2006)Google Scholar
  22. 22.
    Vasuki, V., Natarajan, N., Lu, Z., Savas, B., Dhillon, I.: Scalable affiliation recommendation using auxiliary networks. ACM Trans. Intell. Syst. Technol. 3, 3:1–3:20 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Panagiotis Symeonidis
    • 1
  • Christos Perentis
    • 2
  1. 1.Department of InformaticsAristotle UniversityThessalonikiGreece
  2. 2.Fondazione Bruno KesslerTrentoItaly

Personalised recommendations