Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction

2026-03-19

AI Summary

This work addresses the limitations of existing information popularity prediction methods, which predominantly focus on micro-level cascade features while overlooking macroscopic diffusion dynamics and information heterogeneity. To bridge this gap, the study introduces physics-informed neural networks (PINNs) to model the macroscopic laws governing information propagation for the first time. By integrating graph convolutional networks (GCNs), recurrent neural networks (RNNs), and an adaptive clustering mechanism, the proposed framework jointly captures the structural characteristics of information diffusion and content heterogeneity. This approach transcends the constraints of conventional deep learning models and achieves significantly superior performance over state-of-the-art methods across three real-world datasets, thereby enhancing the accuracy of popularity prediction.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

With society entering the Internet era, the volume and speed of data and information have been increasing. Predicting the popularity of information cascades can help with high-value information delivery and public opinion monitoring on the internet platforms. The current state-of-the-art models for predicting information popularity utilize deep learning methods such as graph convolution networks (GCNs) and recurrent neural networks (RNNs) to capture early cascades and temporal features to predict their popularity increments. However, these previous methods mainly focus on the micro features of information cascades, neglecting their general macroscopic patterns. Furthermore, they also lack consideration of the impact of information heterogeneity on spread popularity. To overcome these limitations, we propose a physics-informed neural network with adaptive clustering learning mechanism, PIACN, for predicting the popularity of information cascades. Our proposed model not only models the macroscopic patterns of information dissemination through physics-informed approach for the first time but also considers the influence of information heterogeneity through an adaptive clustering learning mechanism. Extensive experimental results on three real-world datasets demonstrate that our model significantly outperforms other state-of-the-art methods in predicting information popularity.

Problem

• information popularity prediction
• information cascades
• macroscopic patterns
• information heterogeneity

Innovation

• Physics-Informed Neural Network
• Adaptive Clustering
• Information Popularity Prediction
• Information Heterogeneity
• Macroscopic Pattern Modeling