Inferring Social Bridges that Diffuse Information Across Communities
While the accuracy of link prediction has been improved continuously, the utility of the inferred new links is rarely concerned when it comes to information diffusion. This paper defines the utility of links based on average shortest distance and more importantly defines a special type of links named bridge links based on community structure (overlapping or not) of the network. In sociology, bridge links are considered to play a more crucial role in information diffusion across communities. Considering that the accuracy of previous link prediction methods are high in predicting strong ties but not much high in predicting weak ties, we propose a new link prediction method named iBridge, which aims to infer new diffusion paths using biased structural metrics in a supervised learning framework. The experimental results in 3 real online social networks show that iBridge outperforms the traditional supervised link prediction method especially in inferring the bridge links and meantime, the overall performance of predicting bridge links and non-bridge links is not compromised, thus verifying its robustness in inferring new links.
KeywordsBridge link prediction Information diffusion Weak ties
This research was supported by the National Nature Science Foundation of China (No. 61571238, No. 61603197 and No. 61772284).
- 3.Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: International Conference on World Wide Web, pp. 519–528 (2012)Google Scholar
- 4.Bilgic, M., Mihalkova, L., Getoor, L.: Active learning for networked data. In: International Conference on Machine Learning, pp. 79–86 (2010)Google Scholar
- 6.Brouard, C., D’Alché-Buc, F., Szafranski, M.: Semi-supervised penalized output Kernel regression for link prediction. In: International Conference on Machine Learning, pp. 593–600 (2013)Google Scholar
- 8.Chiu, H.Y., Chen, S.M.: Propagating online social networks: via different kinds of weak ties. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1189–1195 (2013)Google Scholar
- 10.Ferrara, E., Meo, P.D., Fiumara, G., Provetti, A.: The role of strong and weak ties in Facebook: a community structure perspective. Commun. ACM 57(11), 78–84 (2012)Google Scholar
- 12.Hasan, M.A., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: Proceedings of SDM 2006 Workshop on Link Analysis, Counterterrorism and Security (2006)Google Scholar
- 13.Kashima, H., et al.: Link propagation: a fast semi-supervised learning algorithm for link prediction. In: International Conference on World Wide Web, pp. 1099–1110 (2009)Google Scholar
- 22.Song, C., Hsu, W., Lee, M.L.: Mining brokers in dynamic social networks. In: ACM International on Conference on Information and Knowledge Management, pp. 523–532 (2015)Google Scholar