The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks
Open accessAbstract
Influence Maximization (IM), that seeks a small set of key users who spread the influence widely into the network, is a core problem in multiple domains. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. Despite the huge amount of effort, IM in billion-scale networks such as Facebook, Twitter, and World Wide Web has not been satisfactorily solved. Even the state-of-the-art methods such as TIM+ and IMM may take days on those networks. In this paper, we propose SSA and D-SSA, two novel sampling frameworks for IM-based viral marketing problems. SSA and D-SSA are up to 1200 times faster than the SIGMOD'15 best method, IMM, while providing the same (1-1/e-ε) approximation guarantee. Underlying our frameworks is an innovative Stop-and-Stare strategy in which they stop at exponential check points to verify (stare) if there is adequate statistical evidence on the solution quality. Theoretically, we prove that SSA and D-SSA are the first approximation algorithms that use (asymptotically) minimum numbers of samples, meeting strict theoretical thresholds characterized for IM. The absolute superiority of SSA and D-SSA are confirmed through extensive experiments on real network data for IM and another topic-aware viral marketing problem, named TVM.
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Cites
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What is Twitter, a social network or a news media?
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Cost-effective outbreak detection in networks
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Efficient influence maximization in social networks
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Scalable influence maximization for prevalent viral marketing in large-scale social networks
Chen, W., Wang, C., & Wang, Y. (2010) · Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1835804.1835934 · Cited by 1,731
Learning influence probabilities in social networks
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Influence Maximization in Near-Linear Time: A Martingale Approach
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Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
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SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold 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
Metadata
- Authors
- Nguyen, H. T., Thai, M. T., & Dinh, T. N.
- Venue
- Proceedings of the 2016 International Conference on Management of Data
- Publisher
- ACM
- Published
- 2016-06-14
- Pages
- 695-710
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2396832258
- Cited by
- 346
- References
- 19
- FWCI
- 53.25
- Type
- conference paper
- Language
- en
- Open access
- bronze
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