Asymmetric Weighted Cascade Model for Competitive Influence Maximization

We introduce a modified Weighted Cascade model that integrates asymmetric budgets and product scores, providing new insights into the Generalized Asymmetric Influence Maximization problem, which we establish as NP-hard. Our simulations demonstrate that players with higher budgets possess a distinct advantage in networks characterized by larger diameters, whereas players with superior product scores exhibit a significant advantage in networks with smaller diameters. Moreover, we identify a robust linear relationship between graph size and the magnitude of influenced nodes. In densely connected networks we derive bounds for the probabilities of influence that are independent of network size. Our examination of Nash equilibria in this domain underscores the absence of a guaranteed pure Nash equilibrium, suggesting that the strategic enhancement of budgets or product scores may yield more substantial benefits than the pursuit of an optimal strategy in this context.

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Published
2024-10-11
DOI
https://doi.org/10.1109/urtc65039.2024.10937540
Primary Topic
Complex Network Analysis Techniques
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article
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article

Asymmetric Weighted Cascade Model for Competitive Influence Maximization

Vipin Gunda, Archit Mehta
Complex Network Analysis Techniques
article

Asymmetric Weighted Cascade Model for Competitive Influence Maximization

Vipin Gunda, Archit Mehta
article en

Abstract

We introduce a modified Weighted Cascade model that integrates asymmetric budgets and product scores, providing new insights into the Generalized Asymmetric Influence Maximization problem, which we establish as NP-hard. Our simulations demonstrate that players with higher budgets possess a distinct advantage in networks characterized by larger diameters, whereas players with superior product scores exhibit a significant advantage in networks with smaller diameters. Moreover, we identify a robust linear relationship between graph size and the magnitude of influenced nodes. In densely connected networks we derive bounds for the probabilities of influence that are independent of network size. Our examination of Nash equilibria in this domain underscores the absence of a guaranteed pure Nash equilibrium, suggesting that the strategic enhancement of budgets or product scores may yield more substantial benefits than the pursuit of an optimal strategy in this context.

Cornell University (US)
Openalex Percentile: Top 75%
Complex Network Analysis Techniques
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