Measuring the Inspiration Rate of Topics in Bibliographic Networks

  • Livio Bioglio
  • Valentina Rho
  • Ruggero G. PensaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10558)


Information diffusion is a widely-studied topic thanks to its applications to social media/network analysis, viral marketing campaigns, influence maximization and prediction. In bibliographic networks, for instance, an information diffusion process takes place when some authors, that publish papers in a given topic, influence some of their neighbors (coauthors, citing authors, collaborators) to publish papers in the same topic, and the latter influence their neighbors in their turn. This well-accepted definition, however, does not consider that influence in bibliographic networks is a complex phenomenon involving several scientific and cultural aspects. In fact, in scientific citation networks, influential topics are usually considered those ones that spread most rapidly in the network. Although this is generally a fact, this semantics does not consider that topics in bibliographic networks evolve continuously. In fact, knowledge, information and ideas are dynamic entities that acquire different meanings when passing from one person to another. Thus, in this paper, we propose a new definition of influence that captures the diffusion of inspiration within the network. We propose a measure of the inspiration rate called inspiration rank. Finally, we show the effectiveness of our measure in detecting the most inspiring topics in a citation network built upon a large bibliographic dataset.


Information diffusion Topic modeling Citation networks 



This work is partially funded by project MIMOSA (MultIModal Ontology-driven query system for the heterogeneous data of a SmArtcity, “Progetto di Ateneo Torino_call2014_L2_157”, 2015–17).


  1. 1.
    Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.A.: The role of social networks in information diffusion. In: Proceedings of WWW 2012, pp. 519–528. ACM (2012)Google Scholar
  2. 2.
    Barbieri, N., Bonchi, F., Manco, G.: Topic-aware social influence propagation models. Knowl. Inf. Syst. 37(3), 555–584 (2013)CrossRefGoogle Scholar
  3. 3.
    Boguslawski, B., Sarhan, H., Heitzmann, F., Seguin, F., Thuries, S., Billoint, O., Clermidy, F.: Compact interconnect approach for networks of neural cliques using 3D technology. In: Proceedings of IFIP/IEEE VLSI-SoC 2015, pp. 116–121 (2015)Google Scholar
  4. 4.
    Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of ACM SIGKDD 2009, pp. 199–208. ACM (2009)Google Scholar
  5. 5.
    Coates, A., Huval, B., Wang, T., Wu, D.J., Catanzaro, B., Ng, A.Y.: Deep learning with COTS HPC systems. In: Proceedings of ICML 2013, pp. 1337–1345. (2013)Google Scholar
  6. 6.
    Cui, P., Wang, F., Liu, S., Ou, M., Yang, S., Sun, L.: Who should share what?: item-level social influence prediction for users and posts ranking. In: Proceeding of ACM SIGIR 2011, pp. 185–194. ACM (2011)Google Scholar
  7. 7.
    Daley, D.J., Kendall, D.G.: Epidemics and rumours. Nature 208, 1118 (1964)CrossRefGoogle Scholar
  8. 8.
    Gohr, A., Hinneburg, A., Schult, R., Spiliopoulou, M.: Topic evolution in a stream of documents. In: Proceedings of SIAM SDM 2009, pp. 859–870. SIAM (2009)Google Scholar
  9. 9.
    Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Market. Lett. 12(3), 211–223 (2001)CrossRefGoogle Scholar
  10. 10.
    Gruhl, D., Guha, R.V., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: Proceedings of WWW 2004, pp. 491–501. ACM (2004)Google Scholar
  11. 11.
    Gruhl, D., Liben-Nowell, D., Guha, R.V., Tomkins, A.: Information diffusion through blogspace. SIGKDD Explor. 6(2), 43–52 (2004)CrossRefGoogle Scholar
  12. 12.
    Gui, H., Sun, Y., Han, J., Brova, G.: Modeling topic diffusion in multi-relational bibliographic information networks. In: Proceedings of CIKM 2014, pp. 649–658. ACM (2014)Google Scholar
  13. 13.
    He, Q., Chen, B., Pei, J., Qiu, B., Mitra, P., Giles, C.L.: Detecting topic evolution in scientific literature: how can citations help? In: Proceedings of ACM CIKM 2009, pp. 957–966. ACM (2009)Google Scholar
  14. 14.
    Hethcote, H.W.: The mathematics of infectious diseases. SIAM Rev. 42(4), 599–653 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Hoffman, M.D., Blei, D.M., Bach, F.R.: Online learning for latent dirichlet allocation. In: Proceedings of NIPS 2010, pp. 856–864 (2010)Google Scholar
  16. 16.
    Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of ACM SIGKDD 2003, pp. 137–146. ACM (2003)Google Scholar
  17. 17.
    Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. TWEB 1(1), 5 (2007)CrossRefGoogle Scholar
  18. 18.
    Radicchi, F., Fortunato, S., Markines, B., Vespignani, A.: Diffusion of scientific credits and the ranking of scientists. Phys. Rev. E 80, 056103 (2009)CrossRefGoogle Scholar
  19. 19.
    Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50 (2010)Google Scholar
  20. 20.
    Seo, J., Seok, M.: Digital CMOS neuromorphic processor design featuring unsupervised online learning. In: Proceedings of IFIP/IEEE VLSI-SoC 2015, pp. 49–51. IEEE (2015)Google Scholar
  21. 21.
    Shi, X., Tseng, B.L., Adamic, L.A.: Information diffusion in computer science citation networks. In: Proceedings of ICWSM 2009. The AAAI Press (2009)Google Scholar
  22. 22.
    Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15(1), 72–101 (1904)CrossRefGoogle Scholar
  23. 23.
    Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of KDD 2008, pp. 990–998 (2008)Google Scholar
  24. 24.
    Yang, J., Counts, S.: Comparing information diffusion structure in weblogs and microblogs. In: Proceedings of ICWSM 2010. The AAAI Press (2010)Google Scholar
  25. 25.
    Yang, J., Counts, S.: Predicting the speed, scale, and range of information diffusion in twitter. In: Proceedings of ICWSM 2010. The AAAI Press (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TurinTurinItaly

Personalised recommendations