Abstract
The influence maximization problem (IMP) in complex networks is to address finding a set of key nodes that play vital roles in the information diffusion process, and when these nodes are employed as ”seed nodes”, the diffusion effect is maximized. First, this paper presents a refined network centrality measure, a refined shell (RS) index for node ranking, and then proposes an algorithm for identifying key node sets, namely the reject neighbors algorithm (RNA), which consists of two main sequential parts, i.e., node ranking and node selection. The RNA refuses to select multiple-order neighbors of the seed nodes, scatters the selected nodes from each other, and results in the maximum influence of the identified node set on the whole network. Experimental results on real-world network datasets show that the key node set identified by the RNA exhibits significant propagation capability.
Highlights
Many systems in the real world exist in the form of complex networks, ranging from protein interaction networks in living organisms to interstellar gravitational networks in space
Research on the influence maximization problem (IMP) in complex networks is a hot topic in network science, which is known as identification of a key node set or multiple influential nodes, which refers to selecting k initial propagation seed nodes under the premise of a given budget, to maximize the impact on network propagation [1,2]
On the basis of the SIR propagation model, the nodes identified by a key node set identification algorithm are set to infected state (I-state) for propagation simulation, and the differences in propagation range and propagation speed between reject neighbors algorithm (RNA) and eight strategies such as degree centrality (DC), KS, betweenness centrality (BC), closeness centrality (CC), degree centrality coloring (DCC), K-shell coloring (KSC), betweenness centrality coloring (BCC), and closeness centrality coloring (CCC) are compared in turn
Summary
Many systems in the real world exist in the form of complex networks, ranging from protein interaction networks in living organisms to interstellar gravitational networks in space. A survey article by [6] summarized the types of social influence evaluation metrics into centrality measures, link topological ranking measures, and entropy measures. They classified existing IMP algorithms into greedy-based algorithms, heuristic-based algorithms, and others such as voting-based and greedy-based, and heuristic-based hybrid algorithms. To balance the running time and the influence spread of IMP solution, Wei Chen et al proposed a scalable influence maximization solution for viral marketing in large-scale social networks [10]. Proposed a refined k-shell centrality indicator for IMP; Proposed a node ranking and a reject neighbors-based node selection two-phase IMP algorithm; Achieved superior IMP results as compared with other state-of-the-art methods. The main contents of the paper are as follows: First, the process of the reject neighbors algorithm is introduced in detail; in Section 2, and a simple verification is performed on the dolphin social network [19]; subsequently, in Section 3, the relevant contents of the simulation experiment are introduced, including network dataset, evaluation index, comparison algorithm, and experimental results, and so on; Section 4 concludes the whole paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
