Influence Maximization on Complex Networks with Intrinsic Nodal Activation
In many complex networked systems such as online social networks, at any given time, activity originates at certain nodes and subsequently spreads on the network through influence. Under such scenarios, influencer mining does not involve explicit seeding as in the case of viral marketing. Being an influencer necessitates creating content and disseminating the same to active followers who can then spread the same on the network. In this work, we present a simple probabilistic formulation that models such self-evolving systems where information diffusion occurs primarily because of the intrinsic activity of users and the spread of activity occurs due to influence. We provide an algorithm to mine for the influential seeds in such a scenario by modifying the well-known influence maximization framework with the independent cascade diffusion model. A small example is provided to illustrate how the incorporation of intrinsic and influenced activation mechanisms help us better model the influence dynamics in social networks. Following that, for a larger dataset, we compare the lists of influential users identified by the given formulation with a computationally efficient centrality metric derived from a linear probabilistic model that incorporates self activation.
KeywordsComplex networks Influence maximization Social influence Self-activation Centrality Spectral methods
This research was supported by the High Performance Data Analytics program at the Pacific Northwest National Laboratory (PNNL). PNNL is a multi- program national laboratory operated by Battelle Memorial Institute for the US Department of Energy under DE-AC06-76RLO1830.
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