Efficient Greedy Algorithms for Influence Maximization in Social Networks

(I)nfluence maximization is an important problem of finding a small subset of nodes in a social network, such that by targeting this set, one will maximize the expected spread of influence in the network. To improve the efficiency of algorithm KK_ Greedy proposed by Kempe et al., we propose two improved algorithms, Lv_ NewGreedy and Lv_ CELF. By combining all of advantages of these two algorithms, we propose a mixed algorithm Lv_ MixedGreedy. We conducted experiments on two synthetically datasets and show that our improved algorithms have a matching influence with their benchmark algorithms, while being faster than them.