Mining the network value of customers
Proceedings of the seventh 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
Pocket switched networks and human mobility in conference environments
Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking
The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Social network analysis for routing in disconnected delay-tolerant MANETs
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Bubble rap: social-based forwarding in delay tolerant networks
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting information diffusion models over social networks for behavioral analysis
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Influence and passivity in social media
Proceedings of the 20th international conference companion on World wide web
Information resonance on Twitter: watching Iran
Proceedings of the First Workshop on Social Media Analytics
Tractable models for information diffusion in social networks
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
DACCER: Distributed Assessment of the Closeness CEntrality Ranking in complex networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
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The problem of spreading information in social networks is a topic of considerable recent interest, but the conventional influence maximisation problem which selects a set of any arbitrary k nodes in a network as the initially activated nodes might be inadequate in a real-world social network -- cyber-stalkers try to initially spread a rumour through their neighbours only rather than arbitrary users selected from the entire network. To consider this more practical scenario, Kim and Eiko [16] introduced the optimisation problem to find influential neighbours to maximise information diffusion. We extend this model by introducing several important parameters such as user propagation rate on his (or her) neighbours to provide a more general and practical information diffusion model. We performed intensive simulations on several real-world network topologies (emails, blogs, Twitter and Facebook) to develop more effective information spreading schemes under this model. Unlike the results of previous research, our experimental results shows that information can be efficiently propagated in social networks using the propagation rate alone, even without consideration of the "number of friends" information. Moreover, we found that the naive random spreading would be used to efficiently spread information if k increases sufficiently (e.g. k = 4).