Abstract
Micro-blog, as an emerging social network platform, provides good opportunities for viral marketing. For advertisers, an important issue is how to find a small subset of influential users which is called seed set (SS) in social network that can maximize the spread of influence. This problem is considered as “influence maximization”. For advertisers with a limited budget, finding SS quickly and making the spread maximization are both important. To achieve these two goals, we propose the Candidates-Based Greedy (CBG) algorithm. Our approach is composed of two parts: a) for a given size of SS k, all the users are ranked by heuristic methods and the top-N (N >= k) of them who have good spread ability are selected as candidates; b) select SS from the candidates with a greedy algorithm to maximize the influence. In this way the nodes participating in the seed selection of the greedy algorithm are reduced obviously and only the important nodes are reserved, so that the running time is greatly reduced without affecting the accuracy. Our experimental results demonstrate that, comparing with StaticGreedyCELF which is a very efficient greedy algorithm, our algorithm achieves much better running time in micro-blog, almost 70% less, and does not lose any accuracy.
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Ma, Q., Ma, J. (2014). An Efficient Influence Maximization Algorithm to Discover Influential Users in Micro-blog. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_14
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DOI: https://doi.org/10.1007/978-3-319-08010-9_14
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