Combining Path-Constrained Random Walks to Recover Link Weights in Heterogeneous Information Networks

  • Hong-Lan BottermanEmail author
  • Robin Lamarche-Perrin
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


Heterogeneous information networks (HINs) are abstract representations of systems composed of multiple types of entities and their relations. Given a pair of nodes in a HIN, this work aims at recovering the exact weight of the incident link to these two nodes, knowing some other links present in the HINs. Actually, this weight is approximated by a linear combination of probabilities, results of path-constrained random walks, i.e., random walks where the walker is forced to follow only a specific sequence of node types and edge types which is commonly called a meta path, performed on the HINs. This method is general enough to compute the link weight between any types of nodes. Experiments on Twitter data show the applicability of the method.



This work is funded in part by the European Commission H2020 FETPROACT 2016-2017 program under grant 732942 (ODYCCEUS), by the ANR (French National Agency of Research) under grant ANR-15- E38-0001 (AlgoDiv), and by the Île-de-France Region and its program FUI21 under grant 16010629 (iTRAC).


  1. 1.
    Fang, Y., Lin, W., Zheng, V.W., Wu, M., Chang, K.C., Li, X.: Semantic proximity search on graphs with metagraph-based learning. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 277–288 (2016)Google Scholar
  2. 2.
    Gupta, M., Kumar, P., Bhasker, B.: DPRel: a meta-path based relevance measure for mining heterogeneous networks. Inf. Syst. Front. (2017).
  3. 3.
    He, J., Bailey, J., Zhang, R.: Exploiting transitive similarity and temporal dynamics for similarity search in heterogeneous information networks. In: International Conference on Database Systems for Advanced Applications, DASFAA (2014)Google Scholar
  4. 4.
    Hou U.L., Yao, K., Mak, H.F.: Pathsimext: revisiting pathsim in heterogeneous information networks. In: Li, F., Li, G., Hwang, S.-W., Yao, B., Zhang, Z. (eds.) Web-Age Information Management, pp. 38–42. Springer, Cham (2014)Google Scholar
  5. 5.
    Huang, Z., Zheng, Z., Cheng, R., Sun, Y., Mamoulis, N., Li, X.: Meta structure: computing relevance in large heterogeneous information networks. In: Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’16, pp. 1595–1604. ACM, New York (2016)Google Scholar
  6. 6.
    Lao, N., Cohen, W.W.: Relational retrieval using a combination of path-constrained random walks. Mach. Learn. 81(1), 53–67 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Macskassy, S.A.: On the study of social interactions in Twitter. In: Sixth International AAAI Conference on Weblogs and Social Media, ICWSM (2012)Google Scholar
  8. 8.
    Shi, C., Kong, X., Huang, Y., Yu, P.S., Wu, B.: Hetesim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479–2492 (2014)CrossRefGoogle Scholar
  9. 9.
    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’11, pp. 121–128. IEEE Computer Society, Washington (2011)Google Scholar
  10. 10.
    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 Endowment 4(11), 992–1003 (2011)Google Scholar
  11. 11.
    Xiao, D., Meng, X., Li, Y., Shi, C., Wu, B.: AVGSIM: relevance measurement on massive data in heterogeneous networks. J. Theor. Appl. Inf. Technol. 84(1), 101–110 (2016)Google Scholar
  12. 12.
    Zhou, Y., Huang, J., Sun, H., Sun, Y.: Recurrent meta-structure for robust similarity measure in heterogeneous information networks. ArXiv e-prints (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sorbonne Université, CNRSLaboratoire d’Informatique de Paris 6ParisFrance
  2. 2.CNRS, Institut des système complexes de Paris Île-de-France, ISC-PIFParisFrance

Personalised recommendations