Uncertainty-Aware Fuzzy Centrality Measures for Influential Node Identification: A Structural Modeling Approach Toward E-Commerce Applications

TL;DR

This paper introduces uncertainty-aware fuzzy centrality measures to identify influential nodes in large-scale e-commerce networks, addressing the challenge of uncertain and noisy interactions.

Contribution

It proposes a novel structural modeling approach that incorporates uncertainty into centrality measures for better influence analysis in e-commerce platforms.

Findings

Enhanced identification of influential nodes under uncertainty.

Improved robustness of influence measures in noisy environments.

Application potential in online marketing and recommendation systems.

Abstract

In recent years, e-commerce platforms have become one of the most prominent examples of large-scale interaction networks, where understanding influence dynamics among users, products, and digital entities is essential for applications such as online marketing, recommendation systems, and customer behavior analysis. A key challenge in these platforms is that interactions are often uncertain, noisy, and inferred from implicit signals rather than explicitly defined relationships. This uncertainty cannot be effectively captured using deterministic network models...