##基于同理心计算的社会网络建模

Abstract

This paper proposes a novel framework for modeling social networks based on the principles of empathetic computation. Traditional social network models primarily focus on structural aspects and relationships, often neglecting the crucial role of emotions and behaviors in shaping social interactions. This research introduces an empathetic dimension into the modeling process, leveraging machine learning algorithms to learn and represent emotional relationships and behavioral patterns within networks. The core claim is to build a framework capable of simulating human interactions by incorporating emotional factors, thereby facilitating a deeper understanding of complex phenomena observed in social networks. The methodology involves constructing a network where nodes represent individuals and edges represent connections, but crucially, these edges are augmented with associated emotional states and behavioral probabilities. We utilize machine learning techniques – specifically, recurrent neural networks (RNNs) and graph neural networks (GNNs) – to learn the dynamics of these emotional and behavioral relationships. The resulting model can then be used to simulate network behavior, predict individual actions, and explore emergent social phenomena. This approach offers a significant advancement over existing network models by providing a more nuanced and realistic representation of human social dynamics. The research aims to provide a foundation for understanding and predicting social behavior, with potential applications in areas such as social influence, crisis management, and community resilience.