UBLF: An Upper Bound Based Approach to Discover Influential Nodes in Social Networks

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2013 IEEE 13th International Conference on Data Mining (ICDM), 2013, pp. 907 - 916
Issue Date:
2013-01
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Influence maximization, defined as finding a small subset of nodes that maximizes spread of influence in social networks, is NP-hard under both Linear Threshold (LT) and Independent Cascade (IC) models, where a line of greedy/heuristic algorithms have been proposed. The simple greedy algorithm [14] achieves an approximation ratio of 1-1/e. The advanced CELF algorithm [16], by exploiting the sub modular property of the spread function, runs 700 times faster than the simple greedy algorithm on average. However, CELF is still inefficient [4], as the first iteration calls for N times of spread estimations (N is the number of nodes in networks), which is computationally expensive especially for large networks. To this end, in this paper we derive an upper bound function for the spread function. The bound can be used to reduce the number of Monte-Carlo simulation calls in greedy algorithms, especially in the first iteration of initialization. Based on the upper bound, we propose an efficient Upper Bound based Lazy Forward algorithm (UBLF in short), by incorporating the bound into the CELF algorithm. We test and compare our algorithm with prior algorithms on real-world data sets. Experimental results demonstrate that UBLF, compared with CELF, reduces more than 95% Monte-Carlo simulations and achieves at least 2-5 times speed-raising when the seed set is small.
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