Maximizing product adoption in social networks
Proceedings of the fifth ACM international conference on Web search and data mining
Proceedings of the sixth ACM international conference on Web search and data mining
CINEMA: conformity-aware greedy algorithm for influence maximization in online social networks
Proceedings of the 16th International Conference on Extending Database Technology
The bang for the buck: fair competitive viral marketing from the host perspective
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized influence maximization on social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A probability based algorithm for influence maximization in social networks
Proceedings of the 5th Asia-Pacific Symposium on Internetware
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There is significant current interest in the problem of influence maximization: given a directed social network with influence weights on edges and a number k, find k seed nodes such that activating them leads to the maximum expected number of activated nodes, according to a propagation model. Kempe et al. showed, among other things, that under the Linear Threshold Model, the problem is NP-hard, and that a simple greedy algorithm guarantees the best possible approximation factor in PTIME. However, this algorithm suffers from various major performance drawbacks. In this paper, we propose Simpath, an efficient and effective algorithm for influence maximization under the linear threshold model that addresses these drawbacks by incorporating several clever optimizations. Through a comprehensive performance study on four real data sets, we show that Simpath consistently outperforms the state of the art w.r.t. running time, memory consumption and the quality of the seed set chosen, measured in terms of expected influence spread achieved.