🤖 AI Summary
This study addresses the challenge of identifying influential nodes in e-commerce platforms, where user–item interactions are inherently uncertain and noisy, rendering traditional deterministic network models inadequate. To overcome this limitation, the work proposes a novel fuzzy centrality measure that integrates fuzzy graph theory with uncertainty modeling. The method captures implicit interaction relationships through structural modeling of fuzzy connections and incorporates structural embedding techniques to construct a robust influence assessment metric suitable for noisy environments. Experimental evaluation on real-world e-commerce datasets demonstrates that the proposed approach significantly outperforms state-of-the-art centrality algorithms in both accuracy and robustness for influential node identification.
📝 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...