Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Tracking Information Epidemics in Blogspace
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Blogosphere: research issues, tools, and applications
ACM SIGKDD Explorations Newsletter
Blocking links to minimize contamination spread in a social network
ACM Transactions on Knowledge Discovery from Data (TKDD)
Extracting influential nodes for information diffusion on a social network
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Discovering Influential Nodes for SIS Models in Social Networks
DS '09 Proceedings of the 12th International Conference on Discovery Science
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Discovery of super-mediators of information diffusion in social networks
DS'10 Proceedings of the 13th international conference on Discovery science
Social network inference of smartphone users based on information diffusion models
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Which targets to contact first to maximize influence over social network
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Epidemiological modeling of news and rumors on Twitter
Proceedings of the 7th Workshop on Social Network Mining and Analysis
| Hi-index | 0.00 |
We address the problem of efficiently estimating the influence function of initially activated nodes in a social network under the susceptible/ infected/susceptible (SIS) model, a diffusion model where nodes are allowed to be activated multiple times. The computational complexity drastically increases because of this multiple activation property. We solve this problem by constructing a layered graph from the original social network with each layer added on top as the time proceeds, and applying the bond percolation with a pruning strategy. We show that the computational complexity of the proposed method is much smaller than the conventional naive probabilistic simulation method by a theoretical analysis and confirm this by applying the proposed method to two real world networks.