Abstract
Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes’ spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.
Highlights
Complex networks are common in real life and can be used to represent complex systems in many fields
Networks [3] help people gain a deep insight on biochemical reaction, railway networks [4] reveal the structure of railway via complex network methods, social networks show interactions between people [5,6], and international trade network [7] reflects the products trade between countries
Propagation dynamics has always been an important research direction. Many mechanisms, such as epidemic spreading [13,14,15,16], rumor propagation [17,18], social sudden events spreading [19], and e-commercial advertisements, are all closely related to complex network dynamics
Summary
Complex networks are common in real life and can be used to represent complex systems in many fields. A deep understanding and controlling of different complex networks is of great significance in information spreading and network connectivity. By removing some critical nodes, it can greatly reduce the connectivity of the network to restrain the outbreak of epidemics [11]. It would be easier for us to control epidemics spreading. Propagation dynamics has always been an important research direction. Many mechanisms, such as epidemic spreading [13,14,15,16], rumor propagation [17,18], social sudden events spreading [19], and e-commercial advertisements, are all closely related to complex network dynamics. Hamer presented the mass-action principle [21,22]
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