Exploring the Independent Cascade Model and Its Evolution in Social Network Information Diffusion

TL;DR

Addresses the problem of understanding information diffusion in online social networks and the limitations of classic Independent Cascade (IC) models. Surveys IC and its evolution, including temporal, topic-aware, emotion-aware, spatial, social-relationship, dynamic, and deep learning extensions. Combines a structured synthesis of models and extensions to capture time dynamics, content topics, emotions, and structural factors. Demonstrates the practical relevance for marketing, public information campaigns, and mitigation of misinformation in dynamic online environments.

Abstract

This paper delves into the paramount significance of information dissemination within the dynamic realm of social networks. It underscores the pivotal role of information communication models in unraveling the intricacies of data propagation in the digital age. By shedding light on the profound influence of these models, it not only lays the groundwork for exploring various hierarchies and their manifestations but also serves as a catalyst for further research in this formidable field.