Wei Chen
ABSTRACT
Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. In this paper, we study the efficient influence maximization from two complementary directions. One is to improve the original greedy algorithm of [5] and its improvement [7] to further reduce its running time, and the second is to propose new degree discount heuristics that improves influence spread. We evaluate our algorithms by experiments on two large academic collaboration graphs obtained from the online archival database arXiv.org. Our experimental results show that (a) our improved greedy algorithm achieves better running time comparing with the improvement of [7] with matching influence spread, (b) our degree discount heuristics achieve much better influence spread than classic degree and centrality-based heuristics, and when tuned for a specific influence cascade model, it achieves almost matching influence thread with the greedy algorithm, and more importantly (c) the degree discount heuristics run only in milliseconds while even the improved greedy algorithms run in hours in our experiment graphs with a few tens of thousands of nodes.
Based on our results, we believe that fine-tuned heuristics may provide truly scalable solutions to the influence maximization problem with satisfying influence spread and blazingly fast running time. Therefore, contrary to what implied by the conclusion of [5] that traditional heuristics are outperformed by the greedy approximation algorithm, our results shed new lights on the research of heuristic algorithms.
Supplemental Material
- E. Cohen. Size-estimation framework with applications to transitive closure and reachability. J. Comput. Syst. Sci., 55(3):441--453, 1997. Google Scholar
Digital Library
- D. Coppersmith and S. Winograd. Matrix multiplication via arithmetic progressions. J. Symb. Comput., 9(3):251--280, 1990. Google ScholarDigital Library
- P. Domingos and M. Richardson. Mining the network value of customers. In Proceedings of the 7th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 57--66, 2001. Google ScholarDigital Library
- M. Granovetter. Threshold models of collective behavior. American J. of Sociology, 83(6):1420--1443, 1978.Google ScholarCross Ref
- D. Kempe, J. M. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 137--146, 2003. Google ScholarDigital Library
- M. Kimura and K. Saito. Tractable models for information diffusion in social networks. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 259--271, 2006. Google ScholarDigital Library
- J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. S. Glance. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 420--429, 2007. Google ScholarDigital Library
- M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In Proceedings of the 8th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 61--70, 2002. Google ScholarDigital Library
- T. C. Schelling. Micromotives and Macrobehavior. Norton, 1978.Google Scholar
- S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, 1994.Google ScholarCross Ref
Index Terms
- Efficient influence maximization in social networks
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