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Social media forms a central domain for the production and dissemination of real-time information. Even though such flows of information have traditionally been thought of as diffusion processes over social networks, the underlying phenomena are the result of a complex web of interactions among numerous participants. Here, the authors develop a Linear Influence Model where rather than requiring the knowledge of the social network and then modeling the diffusion by predicting which node will influence which other nodes in the network, they focus on modeling the global influence of a node on the rate of diffusion through the (implicit) network. They model the number of newly infected nodes as a function of which other nodes got infected in the past.
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