动态图节点属性预测新框架，能随图结构变化精准预测多步未来状态。
DynaSTy: A Framework for SpatioTemporal Node Attribute Prediction in Dynamic Graphs
- 用可调注意力偏置融合动态邻接矩阵，让模型关注随时间变化的邻居。
- 在多个数据集上，RMSE和MAE均优于现有方法，长程预测误差更小。
- 适合跨主体、跨场景的动态系统预测，如脑网络、金融系统等。
在动态图中准确进行多步节点属性预测对金融信任网络、生物网络等应用至关重要。现有时空图神经网络通常假设邻接矩阵静态不变。本文提出一种端到端的动态边偏置时空模型，输入节点属性多维时序数据与邻接矩阵时序序列，预测未来多步节点属性。模型基于Transformer，在每一步将当前邻接矩阵作为可调节注意力偏置注入，使模型能随图演化聚焦相关邻居。进一步采用掩码节点-时间预训练目标，提升编码器重建缺失特征能力，并使用调度采样和时序加权损失缓解长程预测中的误差累积。与以往工作不同，本模型支持输入样本间图结构动态变化，适用于跨主体（如不同受试者脑网络）、跨场景（不同情境下的金融系统）或演化的社交系统预测。实验表明，该方法在均方根误差（RMSE）和平均绝对误差（MAE）上持续优于强基线。
原文摘要 · Abstract (English)
Accurate multistep forecasting of node-level attributes on dynamic graphs is critical for applications ranging from financial trust networks to biological networks. Existing spatiotemporal graph neural networks typically assume a static adjacency matrix. In this work, we propose an end-to-end dynamic edge-biased spatiotemporal model that ingests a multi-dimensional timeseries of node attributes and a timeseries of adjacency matrices, to predict multiple future steps of node attributes. At each time step, our transformer-based model injects the given adjacency as an adaptable attention bias, allowing the model to focus on relevant neighbors as the graph evolves. We further deploy a masked node-time pretraining objective that primes the encoder to reconstruct missing features, and train with scheduled sampling and a horizon-weighted loss to mitigate compounding error over long horizons. Unlike prior work, our model accommodates dynamic graphs that vary across input samples, enabling forecasting in multi-system settings such as brain networks across different subjects, financial systems in different contexts, or evolving social systems. Empirical results demonstrate that our method consistently outperforms strong baselines on Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).
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