Influence Maximization in Social Networks Based on Non-backtracking Random Walk

Influence maximization is an optimization problem that aims at finding a small set of nodes as seed nodes to maximize the spread of influence in a social network. In this paper, we introduce a new technique called Non-backtracking Random Walk into the influence maximization problem. An optimized algorithm, called NBRW, is devised through sampling nodes when applying restricted non-backtracking walk in network communities. Node's traversing number gives a good estimate of its influence. Our algorithm can be executed in linear time, which beats other influence maximization algorithms. We give extensive experiments under various diffusion models against other state-of-the-art node-selection algorithms. It demonstrates that in terms of influence range and convergent speed our algorithm gives a superior performance under most diffusion models, especially for linear threshold model.