# Rhythm of Opinion: A Hawkes-Graph Framework for Dynamic Propagation Analysis

arXiv: https://arxiv.org/abs/2504.15072 · HTML: https://mlsanity.com/paper/2504.15072/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Representation Learning for Dynamic Graphs: A Survey](https://mlsanity.com/paper/1905.11485/) (2019)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [Novel Multidimensional Models of Opinion Dynamics in Social Networks](https://mlsanity.com/paper/1505.04920/) (2015)
- [SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction](https://mlsanity.com/paper/2102.04627/) (2021)
- [Quantify Influence of Delay in Opinion Transmission of Opinion Leaders on COVID-19 Information Propagation in the Chinese Sina-microblog](https://mlsanity.com/paper/2011.06797/) (2020)

## Built on by (most cited first, 4 of 4)

- [PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulation](https://mlsanity.com/paper/2512.19933/) (2025)
- [Towards Robust Visual Continual Learning with Multi-Prototype Supervision](https://mlsanity.com/paper/2509.16011/) (2025)
- [EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents](https://mlsanity.com/paper/2603.25498/) (2026)
- [HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation](https://mlsanity.com/paper/2605.23043/) (2026)

## Abstract (arXiv)

The rapid development of social media has significantly reshaped the dynamics of public opinion, resulting in complex interactions that traditional models fail to effectively capture. To address this challenge, we propose an innovative approach that integrates multi-dimensional Hawkes processes with Graph Neural Network, modeling opinion propagation dynamics among nodes in a social network while considering the intricate hierarchical relationships between comments. The extended multi-dimensional Hawkes process captures the hierarchical structure, multi-dimensional interactions, and mutual influences across different topics, forming a complex propagation network. Moreover, recognizing the lack of high-quality datasets capable of comprehensively capturing the evolution of public opinion dynamics, we introduce a new dataset, VISTA. It includes 159 trending topics, corresponding to 47,207 posts, 327,015 second-level comments, and 29,578 third-level comments, covering diverse domains such as politics, entertainment, sports, health, and medicine. The dataset is annotated with detailed sentiment labels across 11 categories and clearly defined hierarchical relationships. When combined with our method, it offers strong interpretability by linking sentiment propagation to the comment hierarchy and temporal evolution. Our approach provides a robust baseline for future research.