An Adaptive Semi-local Algorithm for Node Ranking in Large Complex Networks

The issue of node ranking in complex networks is a classical problem that has obtained much attention over the past few decades, and a great variety of methods have consequently been developed. These proposed methods can be roughly categorized into the global and local methods. The global methods are usually time-consuming and the local methods may be inaccurate. In this paper, we propose a novel semi-local algorithm ASLA (Adaptive Semi-Local Algorithm) that seeks a tradeoff between the time efficiency and the ranking accuracy to overcome the limitations of the global and local methods. ASLA is able to adaptively determine the potential influence scope for each node. Then, the influence value of each node is calculated based on such a personalized influence scope. Finally, all the nodes are ranked according to their influence values. To evaluate the performance of ASLA, we have conducted extensive experiments on both synthetic networks and real-world networks, with the results demonstrating that ASLA is not only more efficient than the global methods but also more accurate than the local methods.