Skip to main content

Proposing Ties in a Dense Hypergraph of Academics

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9471)

Abstract

Nearly all personal relationships exhibit a multiplexity where people relate to one another in many different ways. Using a set of faculty CVs from multiple research institutions, we mined a hypergraph of researchers connected by co-occurring named entities (people, places and organizations). This results in an edge-sparse, link-dense structure with weighted connections that accurately encodes faculty department structure. We introduce a novel model that generates dyadic proposals of how well two nodes should be connected based on both the mass and distributional similarity of links through shared neighbors. Similar link prediction tasks have been primarily explored in unipartite settings, but for hypergraphs where hyper-edges out-number nodes 25-to-1, accounting for link similarity is crucial. Our model is tested by using its proposals to recover link strengths from four systematically lesioned versions of the graph. The model is also compared to other link prediction methods in a static setting. Our results show the model is able to recover a majority of link mass in various settings and that it out-performs other link prediction methods. Overall, the results support the descriptive fidelity of our text-mined, named entity hypergraph of multi-faceted relationships and underscore the importance of link similarity in analyzing link-dense multiplexitous relationships.

Keywords

  • Random Forest
  • Social Network Analysis
  • Preferential Attachment
  • Link Prediction
  • Link Similarity

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-27433-1_15
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-27433-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   69.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adamic, L.A., Adar, E.: Friends and neighbors on the web. Social Networks 25(3), 211–230 (2003)

    CrossRef  Google Scholar 

  2. Al Hasan, M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: Workshop on Link Discovery: Issues, Approaches and Applications (2005)

    Google Scholar 

  3. Baronchelli, A., Ferrer-i Cancho, R., Pastor-Satorras, R., Chater, N., Christiansen, M.H.: Networks in cognitive science. Trends in Cognitive Sciences 17(7), 348–360 (2013)

    CrossRef  Google Scholar 

  4. Boccaletti, S., Bianconi, G., Criado, R., Del Genio, C., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., Zanin, M.: The structure and dynamics of multilayer networks. Physics Reports (2014)

    Google Scholar 

  5. Bollobás, B.: Modern graph theory, vol. 184. Springer (1998)

    Google Scholar 

  6. Breiger, R.L.: The duality of persons and groups. Social Forces 53(2), 181–190 (1974)

    CrossRef  Google Scholar 

  7. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)

    CrossRef  MATH  Google Scholar 

  8. Chung, F.R.: Spectral graph theory, vol. 92. American Mathematical Soc. (1997)

    Google Scholar 

  9. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)

    MATH  Google Scholar 

  10. Duan, D., Li, Y., Li, R., Lu, Z.: Incremental k-clique clustering in dynamic social networks. Artificial Intelligence Review 38(2), 129–147 (2012)

    CrossRef  Google Scholar 

  11. Dunbar, R.I., Spoors, M.: Social networks, support cliques, and kinship. Human Nature 6(3), 273–290 (1995)

    CrossRef  Google Scholar 

  12. Easley, D., Kleinberg, J.: Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press (2010)

    Google Scholar 

  13. Feld, S.L.: The focused organization of social ties. American Journal of Sociology, 1015–1035 (1981)

    Google Scholar 

  14. Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 363–370. Association for Computational Linguistics (2005)

    Google Scholar 

  15. Fischman, J.: Arizona’s big bet: The research rethink. Nature 514(7522), 292 (2014)

    CrossRef  Google Scholar 

  16. Gerow, A.: Extracting clusters of specialist terms from unstructured text. In: Proceedings of the 2014 Conference on Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), Doha, Qatar, pp. 1426–1434 (2014)

    Google Scholar 

  17. Gerow, A., Evans, J.: The modular community structure of linguistic predication networks. In: Proceedings of TextGraphs-9, Doha, Qatar, pp. 48–54 (2014)

    Google Scholar 

  18. Getoor, L., Diehl, C.P.: Link mining: a survey. ACM SIGKDD Explorations Newsletter 7(2), 3–12 (2005)

    CrossRef  Google Scholar 

  19. Getoor, L., Friedman, N., Koller, D., Taskar, B.: Learning probabilistic models of link structure. The Journal of Machine Learning Research 3, 679–707 (2003)

    MathSciNet  MATH  Google Scholar 

  20. Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)

    CrossRef  MATH  Google Scholar 

  21. Guns, R., Rousseau, R.: Recommending research collaborations using link prediction and random forest classifiers. Scientometrics 101(2), 1461–1473 (2014)

    CrossRef  Google Scholar 

  22. Heintz, B., Chandra, A.: Beyond graphs: toward scalable hypergraph analysis systems. ACM SIGMETRICS Performance Evaluation Review 41(4), 94–97 (2014)

    CrossRef  Google Scholar 

  23. Holland, P.W., Leinhardt, S.: An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association 76(373), 33–50 (1981)

    MathSciNet  CrossRef  MATH  Google Scholar 

  24. Lang, J., Lapata, M.: Similarity-driven semantic role induction via graph partitioning. Computational Linguistics 40(3), 633–669 (2014)

    CrossRef  Google Scholar 

  25. Li, D., Xu, Z., Li, S., Sun, X.: Link prediction in social networks based on hypergraph. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 41–42 (2013)

    Google Scholar 

  26. Li, L., Li, T.: News recommendation via hypergraph learning: encapsulation of user behavior and news content. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 305–314. ACM (2013)

    Google Scholar 

  27. Li, X., Liu, B., Yu, P.S.: Discovering overlapping communities of named entities. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 593–600. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  28. Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology 58(7), 1019–1031 (2007)

    CrossRef  Google Scholar 

  29. 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 

  30. Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940. ACM (2008)

    Google Scholar 

  31. Mika, P.: Ontologies are us: A unified model of social networks and semantics. Web Semantics: Science, Services and Agents on the World Wide Web 5(1), 5–15 (2007)

    CrossRef  Google Scholar 

  32. Mitchum, R., Brand, A., Transande, C.: White paper: Information, interaction, influence: Research information technologies and their role in advancing science (2014)

    Google Scholar 

  33. Newman, M.E.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)

    MathSciNet  CrossRef  MATH  Google Scholar 

  34. Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems 2, 849–856 (2002)

    Google Scholar 

  35. Robins, G., Pattison, P., Kalish, Y., Lusher, D.: An introduction to exponential random graph (p*) models for social networks. Social Networks 29(2), 173–191 (2007)

    CrossRef  Google Scholar 

  36. Sharma, A., Srivastava, J., Chandra, A.: Predicting multi-actor collaborations using hypergraphs. arXiv preprint arXiv:1401.6404 (2014)

  37. Shi, F., Foster, J.G., Evans, J.: Weaving the fabric of science: Dynamic network models of sciences unfolding structure. Social Networks (forthcoming, 2015)

    Google Scholar 

  38. Sintos, S., Tsaparas, P.: Using strong triadic closure to characterize ties in social networks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1466–1475. ACM (2014)

    Google Scholar 

  39. Snijders, T.A.: Markov chain monte carlo estimation of exponential random graph models. Journal of Social Structure 3(2), 1–40 (2002)

    Google Scholar 

  40. Taramasco, C., Cointet, J.-P., Roth, C.: Academic team formation as evolving hypergraphs. Scientometrics 85(3), 721–740 (2010)

    CrossRef  Google Scholar 

  41. Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the conll-2003 shared task: language-independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, vol. 4, pp. 142–147. Association for Computational Linguistics (2003)

    Google Scholar 

  42. Walter, F.E., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. Autonomous Agents and Multi-Agent Systems 16(1), 57–74 (2008)

    CrossRef  Google Scholar 

  43. Xia, F., Chen, Z., Wang, W., Li, J., Yang, L.T.: Mvcwalker: Random walk based most valuable collaborators recommendation exploiting academic factors. IEEE Transcactions on Emerging Topics in Computing 2(3), 364–375 (2014)

    CrossRef  Google Scholar 

  44. Zhang, Z.-K., Liu, C.: A hypergraph model of social tagging networks. Journal of Statistical Mechanics: Theory and Experiment 2010(10), P10005 (2010)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aaron Gerow .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Gerow, A., Lou, B., Duede, E., Evans, J. (2015). Proposing Ties in a Dense Hypergraph of Academics. In: Liu, TY., Scollon, C., Zhu, W. (eds) Social Informatics. SocInfo 2015. Lecture Notes in Computer Science(), vol 9471. Springer, Cham. https://doi.org/10.1007/978-3-319-27433-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27433-1_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27432-4

  • Online ISBN: 978-3-319-27433-1

  • eBook Packages: Computer ScienceComputer Science (R0)