A node influence ranking algorithm based on probability walking model

Abstract

For the limitation that current node influence ranking algorithms can only be applied in a single type of network and the results are inaccurate, an algorithm based on similarity is proposed. When a node is similar to many nodes in the network, it is representative and can be treated as an influential node. Firstly, probability walking model is used to simulate the initiative visit between nodes in different types of networks. Secondly, superposed probabilistic transfer similarity is defined based on the model considering nodes’ inbound and outbound information. Finally, node ranking algorithm is set up using the new similarity measuring method. Experiments show that the algorithm can evaluate different kinds of networks with high accuracy, whether the network is directed or undirected, weighted or unweighted.