Identifying influential nodes in complex networks

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Identifying a set of influential spreaders in complex networks (2016, methodology): “Up to now, many methods for ranking nodes by their influences have been proposed, such as degree, closeness[7], betweenness[8] centralities, and other heuristic algorithms[9, 10, 11, 12].”
Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering (2013, background): “Chen et al. [14] devised a semi-local index by considering the next nearest neighborhood, which can well identify influential nodes in a hierarchical tree and give more elaborate division than k -core decomposition.”
Dynamics of information diffusion and its applications on complex networks (2016, background): “Network topology, such as centrality [279], structural hole [281], k-shell [134], can statistically identify influential leaders with respect to their local roles in networks.”
Vital nodes identification in complex networks (2016, background): “From the results, one can see that LocalRank [33] and eigenvector centrality [99] perform overall better than others.”
Identifying influential spreaders in complex networks based on gravity formula (2015, methodology): “devised a semi-local index by considering the next nearest neighborhood [18]; Lin et al.”