The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
Abstract
Influence maximization is the problem of finding a small set of most influential nodes in a social network so that their aggregated influence in the network is maximized. In this paper, we study influence maximization in the linear threshold model, one of the important models formalizing the behavior of influence propagation in social networks. We first show that computing exact influence in general networks in the linear threshold model is #P-hard, which closes an open problem left in the seminal work on influence maximization by Kempe, Kleinberg, and Tardos, 2003. As a contrast, we show that computing influence in directed a cyclic graphs (DAGs) can be done in time linear to the size of the graphs. Based on the fast computation in DAGs, we propose the first scalable influence maximization algorithm tailored for the linear threshold model. We conduct extensive simulations to show that our algorithm is scalable to networks with millions of nodes and edges, is orders of magnitude faster than the greedy approximation algorithm proposed by Kempe et al. and its optimized versions, and performs consistently among the best algorithms while other heuristic algorithms not design specifically for the linear threshold model have unstable performances on different real-world networks.
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Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
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
Nemhauser, G. L., Wolsey, L. A., & Fisher, M. L. (1978) · Mathematical Programming · doi:10.1007/bf01588971 · Cited by 4,412
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
Self-citationThe Complexity of Enumeration and Reliability Problems
Valiant, L. G. (1979) · SIAM Journal on Computing · doi:10.1137/0208032 · Cited by 2,076
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
Self-citationMining knowledge-sharing sites for viral marketing
Richardson, M., & Domingos, P. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775057 · Cited by 1,612
Learning influence probabilities in social networks
Goyal, A., Bonchi, F., & Lakshmanan, L. V. S. (2010) · Proceedings of the third ACM international conference on Web search and data mining · doi:10.1145/1718487.1718518 · Cited by 1,022
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
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
A data-based approach to social influence maximization
Goyal, A., Bonchi, F., & Lakshmanan, L. V. S. (2011) · Proceedings of the VLDB Endowment · doi:10.14778/2047485.2047492 · Cited by 428
Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model
He, X., Song, G., Chen, W., & Jiang, Q. (2012) · Proceedings of the 2012 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972825.40 · Cited by 403
Self-citationInfluence Maximization in Social Networks When Negative Opinions May Emerge and Propagate
Chen, W., Collins, A., Cummings, R., Ke, T., Liu, Z., Rincon, D., Sun, X., Wang, Y., Wei, W., & Yuan, Y. (2011) · Proceedings of the 2011 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972818.33 · Cited by 352
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Metadata
- Authors
- Chen, W., Yuan, Y., & Zhang, L.
- Venue
- 2010 IEEE International Conference on Data Mining
- Publisher
- IEEE
- Published
- 2010-12
- Pages
- 88-97
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2108278206
- Cited by
- 924
- References
- 16
- FWCI
- 22.27
- Type
- conference paper
- Language
- en
- Open access
- closed
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