The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model
Abstract
In many real-world situations, different and often opposite opinions, innovations, or products are competing with one another for their social influence in a networked society. In this paper, we study competitive influence propagation in social networks under the competitive linear threshold (CLT) model, an extension to the classic linear threshold model. Under the CLT model, we focus on the problem that one entity tries to block the influence propagation of its competing entity as much as possible by strategically selecting a number of seed nodes that could initiate its own influence propagation. We call this problem the influence blocking maximization (IBM) problem. We prove that the objective function of IBM in the CLT model is submodular, and thus a greedy algorithm could achieve 1 — 1/e approximation ratio. However, the greedy algorithm requires Monte-Carlo simulations of competitive influence propagation, which makes the algorithm not efficient. We design an efficient algorithm CLDAG, which utilizes the properties of the CLT model, to address this issue. We conduct extensive simulations of CLDAG, the greedy algorithm, and other baseline algorithms on real-world and synthetic datasets. Our results show that CLDAG is able to provide best accuracy in par with the greedy algorithm and often better than other algorithms, while it is two orders of magnitude faster than the greedy algorithm.
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Cites
Maximizing the spread of influence through a social network
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Cost-effective outbreak detection in networks
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Efficient influence maximization in social networks
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Self-citationScalable influence maximization for prevalent viral marketing in large-scale social networks
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Self-citationMining knowledge-sharing sites for viral marketing
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Ergodic Theorems for Weakly Interacting Infinite Systems and the Voter Model
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Cited by
Influence Maximization on Social Graphs: A Survey
Li, Y., Fan, J., Wang, Y., & Tan, K.-L. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807843 · Cited by 659
Combating Fake News: A Survey on Identification and Mitigation Techniques
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3305260 · Cited by 514
Metadata
- Authors
- He, X., Song, G., Chen, W., & Jiang, Q.
- Venue
- Proceedings of the 2012 SIAM International Conference on Data Mining
- Published
- 2012-04-26
- Pages
- 463-474
- OpenAlex
- W2395439732
- Cited by
- 403
- References
- 21
- FWCI
- 21.78
- Type
- conference paper
- Language
- en
- Open access
- closed
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- He, X., Song, G., Chen, W., & Jiang, Q. (2012). Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model. Proceedings of the 2012 SIAM International Conference on Data Mining. https://doi.org/10.1137/1.9781611972825.40
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