Abstract
This is an extension from a selected paper from JSAI2019. Learning social influence between users on social networks has beextensively studied in a decade. Many models were proposed to model the microscopic diffusion process or to directly predict the final diffusion results. However, most of them need expensive Monte Carlo simulations to estimate diffusion results and some of them just predict the size of the spread via regression techniques, where people who will adopt the information becomes unknown. In this work, we regard the prediction of final influence diffusion results in a social network as a classification problem to avoid expensive simulations with knowing the final adopters. Furthermore, we propose a community-based convolutional neural network to capture the information of local structure with the aforementioned network. The proposed model is referred to as the Social Influence Learning on Community-based Convolutional Neural Network, SIL-CCNN. In the experiment, SIL-CCNN shows the promising results in both synthetic and real-world datasets. In addition, modeling local structure is indeed useful for the prediction of information diffusion.
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
References
Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, pp. 519–528 (2012)
Barbieri, N., Bonchi, F., Manco, G.: Topic-aware social influence propagation models. In: Proceedings of the 2012 IEEE 12th International Conference on Data Mining, pp. 81–90 (2012)
Chen, W., Collins, A., Cummings, R., Ke, T., Liu, Z., Rincon, D., Sun, X., Wang, Y., Wei, W., Yuan, Y.: Influence maximization in social networks when negative opinions may emerge and propagate. In: Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 379–390 (2011)
Chen, Y.-C., Ma, H.-S., Huang, J.-W.: Multi-state open opinion model based on positive and negative social influences. In: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 170–177 (2015)
Cheng, J., Adamic, L., Dow, P.A., Kleinberg, J.M., Leskovec, J.: Can cascades be predicted? In: Proceedings of the 23nd International Conference on World Wide Web, pp. 925–936 (2014)
Chou, C.-K., Chen, M.-S.: Multiple factors-aware diffusion in social networks. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 70–81 (2015)
Kempe, D., Kleinberg, J.M., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)
Kingma, D., Adam, J.B.: A method for stochastic optimization. In: Proceedings of International Conference on Learning Representations (2015)
Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E 80, 016118 (2009)
Myers, S.A., Zhu, C., Leskovec, J.: Information diffusion and external influence in networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 33–41 (2012)
Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Proceedings of Knowledge-Based and Intelligent Information and Engineering Systems, pp. 67–75 (2008)
Tsur, O., Rappoport, A.: What’s in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 643–652 (2012)
Wang, L., Ermon, S., Hopcroft, J.E.: Feature-enhanced probabilistic models for diffusion network inference. In: Proceedings of the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 499–514 (2012)
Weng, L., Menczer, F., Ahn, Y.-Y.: Predicting successful memes using network and community structure. In: Proceedings of the Eighth International Conference on Weblogs and Social Media (2014)
Xie, J., Szymanski, B.K.: Towards linear time overlapping community detection in social networks. In: Proceedings of the 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 25–36 (2012)
Yang, M.-H., Chou, C.-K., Chen, M.-S.: Cluster cascades: infer multiple underlying networks using diffusion data. In: Proceedings of International Conference on Advances in Social Networks Analysis and Mining, pp. 281–284 (2014)
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
Tai, S.H., Ma, HS., Huang, JW. (2020). Social Influence Prediction by a Community-Based Convolutional Neural Network. In: Ohsawa, Y., et al. Advances in Artificial Intelligence. JSAI 2019. Advances in Intelligent Systems and Computing, vol 1128. Springer, Cham. https://doi.org/10.1007/978-3-030-39878-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-39878-1_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39877-4
Online ISBN: 978-3-030-39878-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)