Abstract
Minimizing the spread of influence is to find top-k links from a social network such that by blocking them the spread of influence is minimized. Kimura et al. first proposed the problem and presented a greedy algorithm to solve this problem. But the greedy algorithm is too expensive and cannot scale to large scale social networks. In this paper, we propose an efficient algorithm called SA-min based on Simulated Annealing (SA) for the problem. Experimental results on real networks show that our algorithm can outperform the greedy algorithm by more than an order of magnitude while achieving comparable influence spread minimization.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61602159), the Natural Science Foundation of Heilongjiang Province (No. F201430, No. F2015013), the Innovation Talents Project of Science and Technology Bureau of Harbin (No. 2017RAQXJ094, No. 2015RAQXJ004), and the fundamental research funds of universities in Heilongjiang Province (No. HDJCCX-201608).
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Liu, Y., Han, Z., Shi, S., Zhang, W., Xuan, P. (2018). SA-Min: An Efficient Algorithm for Minimizing the Spread of Influence in a Social Network. In: Li, J., et al. Wireless Sensor Networks. CWSN 2017. Communications in Computer and Information Science, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-10-8123-1_29
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DOI: https://doi.org/10.1007/978-981-10-8123-1_29
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