Improving Multi-objective Evolutionary Influence Maximization in Social Networks
In the context of social networks, maximizing influence means contacting the largest possible number of nodes starting from a set of seed nodes, and assuming a model for influence propagation. The real-world applications of influence maximization are of uttermost importance, and range from social studies to marketing campaigns. Building on a previous work on multi-objective evolutionary influence maximization, we propose improvements that not only speed up the optimization process considerably, but also deliver higher-quality results. State-of-the-art heuristics are run for different sizes of the seed sets, and the results are then used to initialize the population of a multi-objective evolutionary algorithm. The proposed approach is tested on three publicly available real-world networks, where we show that the evolutionary algorithm is able to improve upon the solutions found by the heuristics, while also converging faster than an evolutionary algorithm started from scratch.
KeywordsInfluence maximization Social network Multi-objective evolutionary algorithms Seeding
This article is based upon work from COST Action CA15140 ‘Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)’ supported by the COST Agency.
- 3.Grassegger, H., Krogerus, M.: The data that turned the world upside down. Luettu 28 (2017). Luettavissa: http://motherboard.vice.com/read/big-data-cambridge-analytica-brexit-trump
- 7.Bucur, D., Iacca, G., Marcelli, A., Squillero, G., Tonda, A.: Multi-objective evolutionary algorithms for influence maximization in social networks. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017. LNCS, vol. 10199, pp. 221–233. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55849-3_15 CrossRefGoogle Scholar
- 9.Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 199–208. ACM, New York (2009)Google Scholar
- 10.Wang, X., Zhang, X., Zhao, C., Yi, D.: Maximizing the spread of influence via generalized degree discount. In: PloS one (2016)Google Scholar
- 11.Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 420–429, August 2007Google Scholar
- 12.Jiang, Q., Song, G., Cong, G., Wang, Y., Si, W., Xie, K.: Simulated annealing based influence maximization in social networks. In: Burgard, W., Roth, D. (eds.) AAAI. AAAI Press (2011)Google Scholar