Title: Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks,
Abstract: In many complex networked systems, such as online social networks, activity
originates at certain nodes and subsequently spreads on the network through
influence. In this work, we consider the problem of modeling the spread of
influence and the identification of influential entities in a complex network
when nodal activation can happen via two different mechanisms. The first
mechanism of activation stems from factors that are intrinsic to the node. The
second mechanism comes from the influence of connected neighbors. After
introducing the model, we provide an algorithm to mine for the influential
nodes in such a scenario by modifying the well-known influence maximization
algorithm to work with our model that incorporates both forms of activation.
Our model can be considered as a variation of the independent cascade diffusion
model. We provide small motivating examples to facilitate an intuitive
understanding of the effect of including the intrinsic activation mechanism. We
sketch a proof of the submodularity of the influence function under the new
formulation and demonstrate the same on larger graphs. Based on the model, we
explain how influential content creators can drive engagement on social media
platforms. Using additional experiments on a Twitter dataset, we then show how
the formulation can be applied to real-world social media datasets. Finally, we
derive a centrality metric that takes into account, both the mechanisms of
activation and provides for an accurate, computationally efficient, alternate
approach to the problem of identifying influencers under intrinsic activation.