The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Abstract
Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this paper, we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm. Our results from extensive simulations on several real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond million-sized graphs where the greedy algorithm becomes infeasible, and (b) in all size ranges, our algorithm performs consistently well in influence spread --- it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100%--260% increase in influence spread.
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Social big data: recent achievements and new challenges
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016) · Information Fusion · doi:10.1016/j.inffus.2015.08.005 · Cited by 846
Influence Maximization in Near-Linear Time: A Martingale Approach
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Influence Maximization on Social Graphs: A Survey
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Self-citationA data-based approach to social influence maximization
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Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model
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Metadata
- Authors
- Chen, W., Wang, C., & Wang, Y.
- Venue
- Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2010-07-25
- Pages
- 1029-1038
- OpenAlex
- W1984069252
- Cited by
- 1,731
- References
- 18
- FWCI
- 45.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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