Abstract
Identifying the influential spreaders in complex networks is crucial to understand who is responsible for the spreading processes and the influence maximization through networks. Targeting these influential spreaders is significant for designing strategies for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing methods such as local centrality measures like degree centrality are less effective, and global measures like closeness and betweenness centrality could better identify influential spreaders but they have some limitations. In this paper, we propose the HybridRank algorithm using a new hybrid centrality measure for detecting a set of influential spreaders using the topological features of the network. We use the SIR spreading model for simulating the spreading processes in networks to evaluate the performance of our algorithm. Empirical experiments are conducted on real and artificial networks, and the results show that the spreaders identified by our approach are more influential than several benchmarks.
Highlights
Spreading processes are ubiquitous in different complex systems
Qian and Jun propose the hybrid degree centrality, that combine Modified Local Centrality (MLC) which measure node’s distal influence and degree centrality and considers the different ratios between the importance of near-source influence (DC) and distal influence (MLC) under different spreading probabilities, while the spreading probability affect the result of those centralities[14]
We propose a new method named HybridRank to detect the influential spreaders in the network using the topological features of the network
Summary
Spreading processes are ubiquitous in different complex systems. It occurs in a plethora of applications and domains, ranging from the spread of news and ideas to the diffusion of influence and social movements and from the outbreak of a disease to the promotion of commercial products. In the paper of Liu et al named ranking the spreading influence in complex networks, a new method is proposed for measuring the spreading influence of nodes of the same k-core value. Liu et al provide a new method for improving the k-shell centrality by removing the redundant links that leads to densely connect the core nodes but they have a low diffusion importance. We propose a new method named HybridRank to detect the influential spreaders in the network using the topological features of the network. We provide a new hybrid centrality for identifying the influential nodes of the network, and secondly we select a set of the influential spreaders, by interacting all together we maximize the spreading of influence. Linton Freeman proposes the most important contributions for the analysis of social networks
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.
