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

TL;DR

This paper introduces a new influence spread model in complex networks that accounts for both intrinsic node activation and neighbor influence, providing algorithms and metrics for identifying key influencers.

Contribution

It extends the independent cascade model by incorporating intrinsic activation, offers a modified influence maximization algorithm, and proposes a new centrality metric for influencer detection.

Findings

The model captures both intrinsic and neighbor influence mechanisms.

The influence function remains submodular under the new model.

The proposed centrality metric effectively identifies influential nodes in social media datasets.

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.