Title: Temporal Graph Modeling for Information Diffusion

Abstract

Information diffusion is a fundamental process in many social, biological, and technological systems. Understanding and predicting its spread is crucial for various applications, including network analysis, epidemic spread, and social media influence. Traditional approaches to modeling information diffusion often rely on static graphs and fail to capture the dynamic, feedback-driven nature of the process. This paper introduces a novel graph theory framework, Temporal Graph Modeling (TGM), designed to address this limitation by incorporating time-dependent node connections and feedback loops. TGM models the evolution of a graph over time, allowing for precise prediction of information spread based on established mathematical principles. The core mechanism of TGM involves dynamically adjusting the graph structure to represent the impact of feedback and cascading effects. This approach significantly enhances the accuracy of predicting information diffusion compared to existing static models. The paper details the framework's mathematical foundation, provides a comprehensive implementation using a discrete graph representation, and presents preliminary results demonstrating its efficacy in simulating and forecasting information diffusion scenarios. Specifically, we explore the impact of different node connectivity patterns and feedback mechanisms on the resulting information spread.