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Influence Analytics in Graphs

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“Influence analytics in graphs” is the study of the dynamics of influence propagation over a network. Influence includes anything that can be transferred or shaped through network interactions, such as pieces of information, actions, behavior, or opinions. Influence analytics includes modeling of the process of influence propagation and addressing algorithmic challenges that arise in solving optimization problems related to the dynamic propagation process.

Overview

Diffusion process on networks is one of the most important dynamics studied in network analysis, which can represent social influence such as the word-of-mouth effect of a rumor in a social network, the spread of an infectious disease, opinion formation through social interaction, and many other phenomena that could happen online or off-line. Although diffusion process on networks has been studied from the 1960s, in various research areas including sociology and economy, its importance has grown considerably...

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Correspondence to Yuichi Yoshida .

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Yoshida, Y., Tsaparas, P., Lakshmanan, L.V.S. (2019). Influence Analytics in Graphs. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_325

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