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We study network centrality based on dynamic influence propagation models insocial networks. To illustrate our integrated mathematical-algorithmic approachfor understanding the fundamental interplay between dynamic influence processesand static network structures, we focus on two basic centrality measures: (a)Single Node Influence (SNI) centrality, which measures each node's significanceby its influence spread; and (b) Shapley Centrality, which uses the Shapleyvalue of the influence spread function --- formulated based on a fundamentalcooperative-game-theoretical concept --- to measure the significance of nodes.We present a comprehensive comparative study of these two centrality measures.Mathematically, we present axiomatic characterizations, which precisely capturethe essence of these two centrality measures and their fundamental differences.Algorithmically, we provide scalable algorithms for approximating them for alarge family of social-influence instances. Empirically, we demonstrate theirsimilarity and differences in a number of real-world social networks, as wellas the efficiency of our scalable algorithms. Our results shed light on theirapplicability: SNI centrality is suitable for assessing individual influence inisolation while Shapley centrality assesses individuals' performance in groupinfluence settings.

Interplay between Social Influence and Network Centrality: A Comparative Study on Shapley Centrality and Single-Node-Influence Centrality