Social Relation Inference via Label Propagation

  • Yingtao TianEmail author
  • Haochen Chen
  • Bryan Perozzi
  • Muhao Chen
  • Xiaofei Sun
  • Steven Skiena
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)


Collaboration networks are a ubiquitous way to characterize the interactions between people. In this paper, we consider the problem of inferring social relations in collaboration networks, such as the fields that researchers collaborate in, or the categories of projects that Github users work on together. Social relation inference can be formalized as a multi-label classification problem on graph edges, but many popular algorithms for semi-supervised learning on graphs only operate on the nodes of a graph. To bridge this gap, we propose a principled method which leverages the natural homophily present in collaboration networks. First, observing that the fields of collaboration for two people are usually at the intersection of their interests, we transform an edge labeling into node labels. Second, we use a label propagation algorithm to propagate node labels in the entire graph. Once the label distribution for all nodes has been obtained, we can easily infer the label distribution for all edges. Experiments on two large-scale collaboration networks demonstrate that our method outperforms the state-of-the-art methods for social relation inference by a large margin, in addition to running several orders of magnitude faster.


Label propagation Social relation inference Social network 


  1. 1.
    Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems, pp. 2787–2795 (2013)Google Scholar
  2. 2.
    Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 891–900. ACM (2015)Google Scholar
  3. 3.
    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)zbMATHGoogle Scholar
  4. 4.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)Google Scholar
  5. 5.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  6. 6.
    Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)Google Scholar
  7. 7.
    Powell, W.W., White, D.R., Koput, K.W., Owen-Smith, J.: Network dynamics and field evolution: the growth of interorganizational collaboration in the life sciences. Am. J. Sociol. 110(4), 1132–1205 (2005)CrossRefGoogle Scholar
  8. 8.
    Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015)Google Scholar
  9. 9.
    Tang, J., Lou, T., Kleinberg, J.: Inferring social ties across heterogenous networks. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 743–752. ACM (2012)Google Scholar
  10. 10.
    Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998. ACM (2008)Google Scholar
  11. 11.
    Tang, W., Zhuang, H., Tang, J.: Learning to infer social ties in large networks. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6913, pp. 381–397. Springer, Heidelberg (2011). Scholar
  12. 12.
    Tu, C., Zhang, Z., Liu, Z., Sun, M.: Transnet: translation-based network representation learning for social relation extraction. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Melbourne (2017)Google Scholar
  13. 13.
    Wang, F., Zhang, C.: Label propagation through linear neighborhoods. IEEE Trans. Knowl. Data Eng. 20(1), 55–67 (2008)CrossRefGoogle Scholar
  14. 14.
    Xiang, B., Liu, Z., Zhou, J., Li, X.: Feature propagation on graph: a new perspective to graph representation learning. arXiv preprint arXiv:1804.06111 (2018)
  15. 15.
    Xu, L., Wei, X., Cao, J., Philip, S.Y.: On exploring semantic meanings of links for embedding social networks (2018)Google Scholar
  16. 16.
    Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yingtao Tian
    • 1
    Email author
  • Haochen Chen
    • 1
  • Bryan Perozzi
    • 2
  • Muhao Chen
    • 3
  • Xiaofei Sun
    • 1
  • Steven Skiena
    • 1
  1. 1.Department of Computer ScienceStony Brook UniversityStony BrookUSA
  2. 2.Google ResearchNew YorkUSA
  3. 3.Department of Computer ScienceUCLALos AngelesUSA

Personalised recommendations