The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Influence Maximization in Near-Linear Time: A Martingale Approach
Abstract
Given a social network G and a positive integer k, the influence maximization problem asks for k nodes (in G) whose adoptions of a certain idea or product can trigger the largest expected number of follow-up adoptions by the remaining nodes. This problem has been extensively studied in the literature, and the state-of-the-art technique runs in O((k+l) (n+m) log n ε2) expected time and returns a (1-1 e-ε)-approximate solution with at least 1 - 1/n l probability.
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Cites
A note on two problems in connexion with graphs
Dijkstra, E. W. (1959) · Numerische Mathematik · doi:10.1007/bf01386390 · Cited by 24,043
Maximizing the spread of influence through a social network
Kempe, D., Kleinberg, J., & Tardos, É. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956769 · Cited by 7,326
Statistical Models and Methods for Lifetime Data
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(2011) · SpringerReference · doi:10.1007/springerreference_57547 · Cited by 3,190
Mining the network value of customers
Domingos, P., & Richardson, M. (2001) · Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/502512.502525 · Cited by 2,830
Cost-effective outbreak detection in networks
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., & Glance, N. (2007) · Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1281192.1281239 · Cited by 2,502
Efficient influence maximization in social networks
Chen, W., Wang, Y., & Yang, S. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557047 · Cited by 2,156
Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth
Goldenberg, J., Libai, B., & Muller, E. (2001) · Marketing Letters · doi:10.1023/a:1011122126881 · Cited by 1,995
Scalable influence maximization for prevalent viral marketing in large-scale social networks
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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
Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks
Nguyen, H. T., Thai, M. T., & Dinh, T. N. (2016) · Proceedings of the 2016 International Conference on Management of Data · doi:10.1145/2882903.2915207 · Cited by 346
Influence analysis in social networks: A survey
Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.01.005 · Cited by 308
Threat From Being Social: Vulnerability Analysis of Social Network Coupled Smart Grid
Pan, T., Mishra, S., Nguyen, L. N., Lee, G., Kang, J., Seo, J., & Thai, M. T. (2017) · IEEE Access · doi:10.1109/access.2017.2738565 · Cited by 20
Metadata
- Authors
- Tang, Y., Shi, Y., & Xiao, X.
- Venue
- Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
- Publisher
- ACM
- Published
- 2015-05-27
- Pages
- 1539-1554
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2035165116
- Cited by
- 768
- References
- 29
- FWCI
- 62.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tang, Y., Shi, Y., & Xiao, X. (2015). Influence Maximization in Near-Linear Time: A Martingale Approach. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. https://doi.org/10.1145/2723372.2723734
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
