Adpt-STGIN: An Adaptive Spatio-Temporal Graph Inductive Network for Topology-Robust Traffic Prediction in Data Center Networks

Abstract

Accurate network traffic prediction is essential for resource management and congestion control in data center networks. Existing spatio-temporal graph neural network (STGNN) models predominantly employ transductive spatial encoders, such as GCN or GAT, whose parameters are tied to a fixed graph structure, preventing generalization to unseen topologies without full retraining. In this paper, we propose Adpt-STGIN (Adaptive Spatio-Temporal Graph Inductive Network), a topology-robust traffic prediction framework built on two key contributions. First, we design a deeply fused GraphSAGE-GRU cell that embeds independent inductive GraphSAGE(SAmple and aggreGatE) encoders directly into each GRU gate, enabling simultaneous spatio-temporal feature extraction at every time step while remaining topology-agnostic. Second, we develop a topology-robust transfer learning framework with a frozen encoder strategy that adapts pretrained models to new topologies by fine-tuning only the lightweight decoder. Experiments on four data center topologies demonstrate that Adpt-STGIN achieves R^2 > 0.99 in pretraining and generalizes to unseen topologies in zero-shot mode with R^2 > 0.994, confirming the practical efficiency of the proposed framework.