A comparative study on algorithms for influence maximization in social networks

How to disseminate information or ideas through social network connection has received much attention in recent years. The core issue is to find a seed set of initially active individuals that can maximize the influence spread. In this paper, we present a comparative study on three basic algorithms for such issue. Experimental results show that although genetic algorithm can find slightly better solution than other algorithms, it is too time-consuming to be cost-effective. Hence, our on-going work is aimed at improving the search efficiency of different bio-inspired meta-heuristic methods.