Optimizing a static greedy algorithm for influence maximization

Abstract

One of the main problem in social networks and viral marketing is that of finding a set of nodes maximizing the spread of influence. Corresponding algorithms solving this problem are required to have both guaranteed accuracy and high scalability. Greedy algorithms are able to find accurate solutions but fail in efficiency. This paper presents a modification of an existing greedy algorithm to solve the influence maximization problem by integrating a memoization technique. Experimental results with a first prototypical implementation on real-world social networks proved the validity of the proposed technique.

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Cite this paper

@inproceedings{Capone2013OptimizingAS, title={Optimizing a static greedy algorithm for influence maximization}, author={Leonardo Capone and Nicola Di Mauro and Floriana Esposito}, year={2013} }