Causality-based Big Data Analysis of Information Cascade in Communication Networks

This is the authors' abstract. We don't add key points for this paper.

The phenomenon of information cascades in communication networks has been widely studied, with earlier research primarily using statistical models for analysis. Recently, machine learning methods combined with big data analysis have proven to be powerful tools for understanding network behavior. However, previous research efforts have largely overlooked the relationships among various influential factors. In this paper, we extend machine learning based data analysis methods by introducing the concept of causality, termed the Causality-based Big Data Analysis (CBDA) method, which incorporates factors such as fake agents, welfare effects, and trusted versus untrusted prior knowledge to examine their impact on the outcomes of erroneous information cascades. Numerical results demonstrate that each factor significantly influences the outcome, generally increasing the likelihood of its preferred result. This is achieved by clarifying how the weight assigned to each influential factor affects the behavior of subsequent agents. The results of our proposed CBDA method show that trusted prior knowledge significantly reduces the probability of erroneous information cascades, offering superior explainability and effectiveness compared to existing methods.