Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Link Prediction in Temporal Heterogeneous Networks

  • Conference paper
  • First Online:
Intelligence and Security Informatics (PAISI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10241))

Included in the following conference series:

  • 1071 Accesses

Abstract

Link prediction in temporal social networks addresses the problem of predicting future links. The problem of link prediction in heterogeneous networks is challenging due to the existence of multiple types of nodes and edges. There are many methods available in the literature for homogeneous networks, which rely on the network topology. In this work, we extend some of the standard measures viz Common Neighbors, Jaccard Coefficient, AdamicAdar, Time-score, Co-occurrence probabilistic measure and Temporal Co-occurrence probabilistic measure to heterogeneous networks. Probabilistic graphical models prove to be efficient for link prediction compared to topological methods. We incorporate the information related to time of link formation into probabilistic graphical models and generate a new measure called Heterogeneous Temporal Co-occurrence probability (Hetero-TCOP) measure for heterogeneous networks. We evaluate all the extended heterogeneous measures along with Hetero-TCOP on DBLP and HiePh bibliographic networks for predicting two types of links: author-conference/journal links and co-author links in the heterogeneous environment. In both cases, Hetero-TCOP achieves superior performance over the standard topological measures. In the case of DBLP dataset, Hetero-TCOP shows an improvement of 15% accuracy over neighborhood-based measures, 6% over temporal measures and 5% over Co-occurrence probability measure. Similar improvement in performance is observed for HeiPh dataset also.

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
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25, 211–230 (2001)

    Article  Google Scholar 

  2. Benchettara, N., Kanawati, R., Rouveirol, C.: Supervised machine learning applied to link prediction in bipartite social networks. In: 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 326–330. IEEE (2010)

    Google Scholar 

  3. Berlusconi, G., Calderoni, F., Parolini, N., Verani, M., Piccardi, C.: Link prediction in criminal networks: a tool for criminal intelligence analysis. PLOS ONE 4, 1–21 (2016)

    Google Scholar 

  4. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  Google Scholar 

  5. Choudhary, P., Mishra, N., Sharma, S., Patel, R.: Link score: a novel method for time aware link prediction in social network. In: ICDMW (2013)

    Google Scholar 

  6. da Silva Soares, P.R., Prudêncio, R.B.C.: Time series based link prediction. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE (2012)

    Google Scholar 

  7. Davis, D.A., Lichtenwalter, R., Chawla, N.V.: Supervised methods for multi-relational link prediction. Soc. Netw. Anal. Min. 3(2), 127–141 (2013)

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  10. Fire, M., Puzis, R., Elovici, Y.: Link Prediction in Highly Fractional Data Sets, pp. 283–300. Springer, New York (2013)

    Google Scholar 

  11. Gang, F., Ding, Y., Seal, A., Chen, B., Sun, Y., Bolton, E.: Predicting drug target interactions using meta-path-based semantic network analysis. BMC Bioinform. 17(1), 1 (2016)

    Google Scholar 

  12. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

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

    Google Scholar 

  14. Jaya Lakshmi, T., Durga Bhavani, S.: Enhancement to community-based multi-relational link prediction using co-occurrence probability feature. In: Proceedings of the Second ACM IKDD Conference on Data Sciences, CoDS 2015, pp. 86–91. ACM (2015)

    Google Scholar 

  15. Jaya Lakshmi, T., Durga Bhavani, S.: Temporal probabilistic measure for link prediction in collaborative networks. Appl. Intell. 1–13 (2017)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  17. Jaya Lakshmi, T., Durga Bhavani, S.: Heterogeneous link prediction based on multi relational community information. In: Sixth International Conference on Communication Systems and Networks, COMSNETS 2014, pp. 1–4 (2014)

    Google Scholar 

  18. 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 

  19. 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 

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

    Article  Google Scholar 

  21. Lichtenwalter, R., Chawla, N.V.: Link prediction: fair and effective evaluation. In: ASONAM, pp. 376–383. IEEE Computer Society (2012)

    Google Scholar 

  22. Lichtenwalter, R.N., Chawla, N.V.: Vertex collocation profiles: theory, computation, and results. SpringerPlus 3(1), 1–27 (2014)

    Article  Google Scholar 

  23. 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, KDD 2010, pp. 243–252. ACM (2010)

    Google Scholar 

  24. Mooij, J.M.: libDAI: a free and open source C++ library for discrete approximate inference in graphical models. J. Mach. Learn. Res. 11, 2169–2173 (2010)

    MATH  Google Scholar 

  25. Munasinghe, L.: Time-aware methods for link prediction in social networks. Ph.D. thesis, The Graduate University for Advanced Studies (2013)

    Google Scholar 

  26. Munasinghe, L., Ichise, R.: Time aware index for link prediction in social networks. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 342–353. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23544-3_26

    Chapter  Google Scholar 

  27. 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 

  28. 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, pp. 663–672. ACM (2012)

    Google Scholar 

  29. Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4(11), 992–1003 (2011)

    Google Scholar 

  30. Tylenda, T., Angelova, R., Bedathur, S.: Towards time-aware link prediction in evolving social networks. In: Proceedings of the 3rd Workshop on Social Network Mining and Analysis, SNA-KDD 2009, pp. 1–10. ACM (2009)

    Google Scholar 

  31. Valverde-Rebaza, J.C., Andrade Lopes, A.: Link prediction in complex networks based on cluster information. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds.) SBIA 2012. LNCS (LNAI), pp. 92–101. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34459-6_10

    Chapter  Google Scholar 

  32. Wang, C., Satuluri, V., Parthasarathy, S.: Local probabilistic models for link prediction. In: Proceedings of Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 322–331. IEEE Computer Society (2007)

    Google Scholar 

  33. Wang, C., Han, J., Jia, Y., Tang, J., Zhang, D., Yu, Y., Guo, J.: Mining advisor-advisee relationships from research publication networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 203–212. ACM (2010)

    Google Scholar 

  34. Wang, P., BaoWen, X., YuRong, W., Zhou, X.Y.: Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 1(58), 1–38 (2015)

    Google Scholar 

  35. Yang, Y., Chawla, N.V., Sun, Y., Han, J.: Predicting links in multi-relational and heterogeneous networks. In: 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, 10–13 December 2012, pp. 755–764 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

    T. Jaya Lakshmi & S. Durga Bhavani

  2. Vasireddy Venkatadri Institute of Technology, Nambur, Andhra Pradesh, India

    T. Jaya Lakshmi

Authors
  1. T. Jaya Lakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Durga Bhavani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Jaya Lakshmi .

Editor information

Editors and Affiliations

  1. Virginia Tech , Blacksburg, Virginia, USA

    G. Alan Wang

  2. The University of Hong Kong, Hong Kong, China

    Michael Chau

  3. The University of Arizona, Tucson, Arizona, USA

    Hsinchun Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Jaya Lakshmi, T., Durga Bhavani, S. (2017). Link Prediction in Temporal Heterogeneous Networks. In: Wang, G., Chau, M., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2017. Lecture Notes in Computer Science(), vol 10241. Springer, Cham. https://doi.org/10.1007/978-3-319-57463-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57463-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57462-2

  • Online ISBN: 978-3-319-57463-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation