Abstract
Recently, the attention has been increased to the study of the connectivity properties and to the topology of the complex networks. This paper studies the relationship between the influential node and the topological structure of a network. Identification of influential nodes receives paramount interest as it is important for many real-world applications to identify strategically important nodes in different networks including social networks. Several node centrality measures are there for the identification of influential node. To overcome the limitations of well-known centrality measures like degree centrality, closeness centrality, and betweenness centrality, two more techniques are considered; one based on local properties of nodes (local-area centrality) and another based on global properties of the network (structural centrality). This paper investigates the role of local properties and position of the node with respect to the entire network for influential node identification with these algorithms. The experimental result shows that local-area centrality and structural centrality algorithms are able to identify the influential node with less computation time and effectiveness compared to other algorithms.
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Raychaudhuri, A., Mallick, S., Sircar, A., Singh, S. (2020). Identifying Influential Nodes Based on Network Topology: A Comparative Study. In: Mandal, J., Bhattacharya, K., Majumdar, I., Mandal, S. (eds) Information, Photonics and Communication. Lecture Notes in Networks and Systems, vol 79. Springer, Singapore. https://doi.org/10.1007/978-981-32-9453-0_7
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DOI: https://doi.org/10.1007/978-981-32-9453-0_7
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