Skip to main content
Springer Nature Link
Log in

Relationship Prediction in Dynamic Heterogeneous Information Networks

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11437))

Included in the following conference series:

  • 3241 Accesses

Abstract

Most real-world information networks, such as social networks, are heterogeneous and as such, relationships in these networks can be of different types and hence carry differing semantics. Therefore techniques for link prediction in homogeneous networks cannot be directly applied on heterogeneous ones. On the other hand, works that investigate link prediction in heterogeneous networks do not necessarily consider network dynamism in sequential time intervals. In this work we propose a technique that leverages a combination of latent and topological features to predict a target relationship between two nodes in a dynamic heterogeneous information network. Our technique, called MetaDynaMix, effectively combines meta path-based topology features and inferred latent features that incorporate temporal network changes in order to capture network (1) heterogeneity and (2) temporal evolution, when making link predictions. Our experiment results on two real-world datasets show statistically significant improvement over AUCROC and prediction accuracy compared to the state of the art techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Al Hasan, M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: SDM06: Workshop on Link Analysis, Counter-Terrorism and Security (2006)

    Google Scholar 

  2. Al Hasan, M., Zaki, M.J.: A survey of link prediction in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 243–275. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-8462-3_9

    Chapter  Google Scholar 

  3. Cantador, I., Brusilovsky, P., Kuflik, T.: 2nd workshop on information heterogeneity and fusion in recommender systems (HetRec 2011). In: Proceedings of the 5th ACM Conference on Recommender Systems, RecSys 2011, ACM. New York (2011). http://www.grouplens.org

  4. Chen, H., Li, X., Huang, Z.: Link prediction approach to collaborative filtering. In: Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2005, pp. 141–142. IEEE (2005)

    Google Scholar 

  5. Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240. ACM (2006)

    Google Scholar 

  6. Denny, J.C.: Mining electronic health records in the genomics era. PLoS Comput. Biol. 8(12), e1002823 (2012)

    Article  Google Scholar 

  7. Dunlavy, D.M., Kolda, T.G., Acar, E.: Temporal link prediction using matrix and tensor factorizations. ACM Trans. Knowl. Discovery Data (TKDD) 5(2), 10 (2011)

    Google Scholar 

  8. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008). https://github.com/cjlin1/liblinear

    MATH  Google Scholar 

  9. Fire, M., Katz, G., Rokach, L., Elovici, Y.: Links reconstruction attack. In: Altshuler, Y., Elovici, Y., Cremers, A., Aharony, N., Pentland, A. (eds.) Security and Privacy in Social Networks, pp. 181–196. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-4139-7_9

    Chapter  Google Scholar 

  10. Fu, W., Song, L., Xing, E.P.: Dynamic mixed membership blockmodel for evolving networks. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 329–336. ACM (2009)

    Google Scholar 

  11. Gallagher, B., Tong, H., Eliassi-Rad, T., Faloutsos, C.: Using ghost edges for classification in sparsely labeled networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 256–264. ACM (2008)

    Google Scholar 

  12. Guimerà, R., Sales-Pardo, M.: Missing and spurious interactions and the reconstruction of complex networks. Proc. Nat. Acad. Sci. 106(52), 22073–22078 (2009)

    Article  Google Scholar 

  13. Guy, I.: Social recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 511–543. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_15

    Chapter  Google Scholar 

  14. Hay, M., Miklau, G., Jensen, D., Towsley, D., Weis, P.: Resisting structural re-identification in anonymized social networks. Proc. VLDB Endowment 1(1), 102–114 (2008)

    Article  Google Scholar 

  15. Lei, C., Ruan, J.: A novel link prediction algorithm for reconstructing protein-protein interaction networks by topological similarity. Bioinformatics 29(3), 355–364 (2012)

    Article  Google Scholar 

  16. Leroy, V., Cambazoglu, B.B., Bonchi, F.: Cold start link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 393–402. ACM (2010)

    Google Scholar 

  17. Li, X., Chen, H.: Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis. Support Syst. 54(2), 880–890 (2013)

    Article  Google Scholar 

  18. Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)

    Article  Google Scholar 

  19. Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 243–252. ACM (2010)

    Google Scholar 

  20. Lü, L., Medo, M., Yeung, C.H., Zhang, Y.C., Zhang, Z.K., Zhou, T.: Recommender systems. Phys. Rep. 519(1), 1–49 (2012)

    Article  Google Scholar 

  21. McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153–157 (1947)

    Article  Google Scholar 

  22. Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6912, pp. 437–452. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23783-6_28

    Chapter  Google Scholar 

  23. Milani Fard, A., Wang, K.: Neighborhood randomization for link privacy in social network analysis. World Wide Web 18(1), 9–32 (2015)

    Article  Google Scholar 

  24. Milani Fard, A., Wang, K., Yu, P.S.: Limiting link disclosure in social network analysis through subgraph-wise perturbation. In: Proceedings of the International Conference on Extending Database Technology (EDBT), pp. 109–119. ACM (2012)

    Google Scholar 

  25. Qi, G.J., Aggarwal, C.C., Huang, T.: Link prediction across networks by biased cross-network sampling. In: IEEE 29th International Conference on Data Engineering (ICDE), pp. 793–804. IEEE (2013)

    Google Scholar 

  26. Sarkar, P., Moore, A.W.: Dynamic social network analysis using latent space models. ACM SIGKDD Explor. Newsl. 7(2), 31–40 (2005)

    Article  Google Scholar 

  27. Shi, C., Kong, X., Huang, Y., Philip, S.Y., Wu, B.: HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479–2492 (2014)

    Article  Google Scholar 

  28. Shi, C., Li, Y., Zhang, J., Sun, Y., Philip, S.Y.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2017)

    Article  Google Scholar 

  29. Song, H.H., Cho, T.W., Dave, V., Zhang, Y., Qiu, L.: Scalable proximity estimation and link prediction in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet measurement, pp. 322–335. ACM (2009)

    Google Scholar 

  30. Sun, Y., Barber, R., Gupta, M., Aggarwal, C.C., Han, J.: Co-author relationship prediction in heterogeneous bibliographic networks. In: Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, pp. 121–128. IEEE Computer Society (2011)

    Google Scholar 

  31. Sun, Y., Han, J., Aggarwal, C.C., Chawla, N.V.: When will it happen? Relationship prediction in heterogeneous information networks. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 663–672. ACM, New York (2012)

    Google Scholar 

  32. Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: PathSim: meta path-based top-k similarity search in heterogeneous information networks. In: Proceedings of the VLDB Endowment, vol. 4, no. 11, pp. 992–1003. VLDB Endowment (2011)

    Google Scholar 

  33. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: KDD 2008, pp. 990–998 (2008). https://aminer.org/citation

  34. Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: ICDM, pp. 322–331. IEEE (2007)

    Google Scholar 

  35. Wang, C., Song, Y., El-Kishky, A., Roth, D., Zhang, M., Han, J.: Incorporating world knowledge to document clustering via heterogeneous information networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1215–1224. ACM (2015)

    Google Scholar 

  36. Wang, C., Song, Y., Li, H., Zhang, M., Han, J.: Text classification with heterogeneous information network kernels. In: AAAI, pp. 2130–2136 (2016)

    Google Scholar 

  37. Ye, J., Cheng, H., Zhu, Z., Chen, M.: Predicting positive and negative links in signed social networks by transfer learning. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1477–1488. ACM (2013)

    Google Scholar 

  38. Yin, J., Ho, Q., Xing, E.P.: A scalable approach to probabilistic latent space inference of large-scale networks. In: Advances in Neural Information Processing Systems, pp. 422–430 (2013)

    Google Scholar 

  39. Zhang, J., Wang, C., Wang, J., Yu, J.X.: Inferring continuous dynamic social influence and personal preference for temporal behavior prediction. Proc. VLDB Endowment 8(3), 269–280 (2014)

    Article  Google Scholar 

  40. Zheleva, E., Getoor, L.: Preserving the privacy of sensitive relationships in graph data. In: Bonchi, F., Ferrari, E., Malin, B., Saygin, Y. (eds.) PInKDD 2007. LNCS, vol. 4890, pp. 153–171. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78478-4_9

    Chapter  Google Scholar 

  41. Zhu, L., Guo, D., Yin, J., Steeg, G.V., Galstyan, A.: Scalable temporal latent space inference for link prediction in dynamic social networks. IEEE Trans. Knowl. Data Eng. (TKDE) 28(10), 2765–2777 (2016). https://github.com/linhongseba/Temporal-Network-Embedding

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. New York Institute of Technology, Vancouver, Canada

    Amin Milani Fard

  2. Ryerson University, Toronto, Canada

    Ebrahim Bagheri

  3. Simon Fraser University, Burnaby, Canada

    Ke Wang

Authors
  1. Amin Milani Fard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ebrahim Bagheri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ke Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amin Milani Fard .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Milani Fard, A., Bagheri, E., Wang, K. (2019). Relationship Prediction in Dynamic Heterogeneous Information Networks. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15712-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15711-1

  • Online ISBN: 978-3-030-15712-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics