A Linear Time Algorithm for Influence Maximization in Large-Scale Social Networks

Influence maximization is the problem of finding k seed nodes in a given network as information sources so that the influence cascade can be maximized. To solve this problem both efficiently and effectively, in this paper we propose LAIM: a linear time algorithm for influence maximization in large-scale social networks. Our LAIM algorithm consists of two parts: (1) influence computation; and (2) seed nodes selection. The first part approximates the influence of any node using its local influence, which can be efficiently computed with an iterative algorithm. The second part selects seed nodes in a greedy manner based on the results of the first part. We theoretically prove that the time and space complexities of our algorithm are proportional to the network size. Experimental results on six real-world datasets show that our approach significantly outperforms other state-of-the-art algorithms in terms of influence spread, running time and memory usage.