Abstract
In this research, we propose a novel approach for the topic’s propagation prediction on a bibliographic network with a combination of external factors and intrinsic factors. We utilize a supervised method to predict the propagation of a specific topic where combining dissimilar features with the dissimilar measuring coefficient. Firstly, we propose a new method to calculate activation probability from an active node to an inactive node based on both meta-path and textual information. This activation probability is considered as an external factor. Moreover, we exploit the author’s interest in the topic, which is propagated as an intrinsic factor. Finally, we amalgamate the activation probability feature and the author’s preference feature in the topic’s spreading prediction. We conducted experiments on dissimilar topics of the bibliographic network dataset and have attained satisfactory results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akula, R., Yousefi, N., Garibay, I.: DeepFork: Supervised Prediction of Information Diffusion in GitHub, p. 12 (2019)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14, pp. 601–608. MIT Press, Cambridge (2002). http://papers.nips.cc/paper/2070-latent-dirichlet-allocation.pdf
Bui, Q.V., Sayadi, K., Amor, S.B., Bui, M.: Combining latent dirichlet allocation and k-means for documents clustering: effect of probabilistic based distance measures. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) ACIIDS 2017. LNCS (LNAI), vol. 10191, pp. 248–257. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54472-4_24
Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12(3), 211–223 (2001). https://doi.org/10.1023/A:1011122126881
Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978). https://www.journals.uchicago.edu/doi/abs/10.1086/226707
Gui, H., Sun, Y., Han, J., Brova, G.: Modeling topic diffusion in multi-relational bibliographic information networks. In: CIKM (2014)
Ho, T.K.T., Bui, Q.V., Bui, M.: Homophily independent cascade diffusion model based on textual information. In: Nguyen, N.T., Pimenidis, E., Khan, Z., Trawiński, B. (eds.) ICCCI 2018. LNCS (LNAI), vol. 11055, pp. 134–145. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98443-8_13
Kempe, D., Kleinberg, J., Tardos, É.: Influential nodes in a diffusion model for social networks. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005). https://doi.org/10.1007/11523468_91
Kempe, D., Kleinberg, J.M., Tardos, V.: Maximizing the spread of influence through a social network. In: KDD (2003)
Macy, M.W.: Chains of cooperation: threshold effects in collective action. Am. Sociol. Rev. 56(6), 730–747 (1991). https://www.jstor.org/stable/2096252
Molaei, S., Babaei, S., Salehi, M., Jalili, M.: Information spread and topic diffusion in heterogeneous information networks. Sci. Rep. 8(1), 1–14 (2018). https://www.nature.com/articles/s41598-018-27385-2
Molaei, S., Zare, H., Veisi, H.: Deep learning approach on information diffusion in heterogeneous networks. Knowl.-Based Syst., p. 105153 (2019). http://www.sciencedirect.com/science/article/pii/S0950705119305076
Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The Author-Topic Model for Authors and Documents. arXiv:1207.4169 [cs, stat] (2012)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988). http://www.sciencedirect.com/science/article/pii/0306457388900210
Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. In: VLDB 11 (2011)
Varshney, D., Kumar, S., Gupta, V.: Modeling information diffusion in social networks using latent topic information. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2014. LNCS, vol. 8588, pp. 137–148. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09333-8_16
Yang, H.: Mining social networks using heat diffusion processes for marketing candidates selection. ACM (2008). https://aran.library.nuigalway.ie/handle/10379/4164
Acknowledgement
This study is funded by Research Project No. DHH2020-01-164 of Hue University, Vietnam.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bui, Q.V., Ho, T.K.T., Bui, M. (2020). Topic Diffusion Prediction on Bibliographic Network: New Approach with Combination Between External and Intrinsic Factors. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-63007-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63006-5
Online ISBN: 978-3-030-63007-2
eBook Packages: Computer ScienceComputer Science (R0)