The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
A data-based approach to social influence maximization
Abstract
Influence maximization is the problem of finding a set of users in a social network, such that by targeting this set, one maximizes the expected spread of influence in the network. Most of the literature on this topic has focused exclusively on the social graph, overlooking historical data, i.e., traces of past action propagations. In this paper, we study influence maximization from a novel data-based perspective. In particular, we introduce a new model, which we call credit distribution , that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread. Our approach also learns the different levels of influence-ability of users, and it is time-aware in the sense that it takes the temporal nature of influence into account. We show that influence maximization under the credit distribution model is NP -hard and that the function that defines expected spread under our model is submodular. Based on these, we develop an approximation algorithm for solving the influence maximization problem that at once enjoys high accuracy compared to the standard approach, while being several orders of magnitude faster and more scalable.
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Cites
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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
An analysis of approximations for maximizing submodular set functions—I
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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
TwitterRank: finding topic-sensitive influential twitterers
Weng, J., Lim, E.-P., Jiang, J., & He, Q. (2010) · Proceedings of the third ACM international conference on Web search and data mining · doi:10.1145/1718487.1718520 · Cited by 1,742
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
Everyone's an influencer: quantifying influence on twitter
Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935845 · Cited by 1,689
A matrix factorization technique with trust propagation for recommendation in social networks
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Cited by
Influence Maximization in Near-Linear Time: A Martingale Approach
Tang, Y., Shi, Y., & Xiao, X. (2015) · Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data · doi:10.1145/2723372.2723734 · Cited by 768
Metadata
- Authors
- Goyal, A., Bonchi, F., & Lakshmanan, L. V. S.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2011-09
- Volume
- 5
- Issue
- 1
- Pages
- 73-84
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W1991635064
- Cited by
- 428
- References
- 15
- FWCI
- 24.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Goyal, A., Bonchi, F., & Lakshmanan, L. V. S. (2011). A data-based approach to social influence maximization. Proceedings of the VLDB Endowment. https://doi.org/10.14778/2047485.2047492
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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