The m-Ranking of nodes in complex networks

The knowledge of node's ability and importance in spreading information in a complex network is important for developing efficient methods either to decelerate spreading in the case of diseases or to accelerate spreading in the case of information flow, which would benefit the whole population. Some systems are highly affected by a small fraction of influential nodes. Number of fast and efficient spreaders in a network is much less compared to the number of ordinary members. Information about the influential spreaders is significant in the planning for the control of propagation of critical pieces of information in a social or information network. Identifying important members who act as the fastest and efficient spreaders is the focal theme of a large number of research papers. Researchers have identified approximately 10 different methods for this purpose. Degree centrality, closeness centrality, betweenness centrality, k-core decomposition, mixed degree decomposition, improved k-shell decomposition, etc., are some of these methods. In this expository article, we review all previous works done in the field of identifying potential spreaders in a network.

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“…In Ref. [18], the authors have verified the reliability of this method using rank correlation method.…”

Section: Graph Theory -Advanced Algorithms and Applications 14mentioning

confidence: 99%

“…In a recent article, Reji Kumar et al [18] proposed a method in which degree and weights of all vertices and edges get relative representation in a network. This method is named as m-ranking method.…”

Section: Weighted K-shell Decompositionmentioning

confidence: 99%