Optimizing CELF Algorithm for Influence Maximization Problem in Social Networks

The influence maximization problem in social networks aims to find a minimal set of individuals in order to produce the highest influence on the other individuals in the network. In the last two decades, a lot of algorithms have been proposed to solve the time efficiency and effectiveness challenges of this NP-Hard problem. Undoubtedly, the CELF algorithm (besides the naive greedy algorithm) has the highest effectiveness among them. Of course, the CELF algorithm is faster than the naive greedy algorithm (about 700 times). This superiority has led many researchers to make extensive use of the CELF algorithm in their innovative approaches. However, the main drawback of the CELF algorithm is the very long running time of its first iteration since it has to estimate the influence spread for all nodes by the expensive Monte-Carlo simulations, similar to the naive greedy algorithm. In this paper, a heuristic approach is proposed, namely optimized-CELF algorithm, in order to improve this drawback of the CELF algorithm by avoiding the unnecessary Monte-Carlo simulations. It is found that the proposed algorithm reduces the CELF running time, and subsequently, improves the time efficiency of the other algorithms that have employed CELF as a base algorithm. The experimental results on the wide spectral of real datasets show that the optimized-CELF algorithm provides a better running time gain, about 88-99% and 56-98% for k=1 and k=50, respectively, compared to the CELF algorithm without missing effectiveness.