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.
Abstract (translated)
随着社会进入互联网时代，数据与信息的体量与传播速度持续增长。预测信息级联的流行度有助于互联网平台实现高价值信息推送与网络舆情监测。当前预测信息流行度的前沿模型多采用图卷积网络（GCNs）与循环神经网络（RNNs）等深度学习方法，通过捕捉早期级联结构与时序特征来预测流行度增量。然而，既有方法主要聚焦于信息级联的微观特征，忽视其宏观普适模式，同时缺乏对信息异质性对传播流行度影响的考量。为突破上述局限，本研究提出一种融合物理信息与自适应聚类学习机制的神经网络模型（PIACN），用于预测信息级联流行度。该模型首次通过物理信息建模方法刻画信息传播的宏观规律，并借助自适应聚类学习机制纳入信息异质性影响。基于三个真实数据集的 extensive 实验表明，本模型在信息流行度预测任务中显著优于其他前沿方法。
URL
https://arxiv.org/abs/2603.19599
