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In many complex networked systems, such as online social networks, activityoriginates at certain nodes and subsequently spreads on the network throughinfluence. In this work, we consider the problem of modeling the spread ofinfluence and the identification of influential entities in a complex networkwhen nodal activation can happen via two different mechanisms. The firstmechanism of activation stems from factors that are intrinsic to the node. Thesecond mechanism comes from the influence of connected neighbors. Afterintroducing the model, we provide an algorithm to mine for the influentialnodes in such a scenario by modifying the well-known influence maximizationalgorithm to work with our model that incorporates both forms of activation.Our model can be considered as a variation of the independent cascade diffusionmodel. We provide small motivating examples to facilitate an intuitiveunderstanding of the effect of including the intrinsic activation mechanism. Wesketch a proof of the submodularity of the influence function under the newformulation and demonstrate the same on larger graphs. Based on the model, weexplain how influential content creators can drive engagement on social mediaplatforms. Using additional experiments on a Twitter dataset, we then show howthe formulation can be applied to real-world social media datasets. Finally, wederive a centrality metric that takes into account, both the mechanisms ofactivation and provides for an accurate, computationally efficient, alternateapproach to the problem of identifying influencers under intrinsic activation.

Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks