Topology-Aware Graph Neural Network with Spatiotemporal Encoding and Contrastive Learning for Scalable Dynamic Network Representation
TL;DR
The implications of the proposed TAGNN framework on such applications as anomaly detection, social media analytics, and traffic forecasting are that the proposed framework provides an end-to-end solution that promotes accuracy, efficiency, and temporal consistency in dynamic graph learning.
Abstract
Recently, Dynamic Graph Neural Networks (DGNNs) have become a potent candidate to model time-changing graphs, although other solutions, including Temporal Graph Networks (TGN), Evolving Graph Convolutional Networks (EvolveGCN), and Dynamic Self-Attention Networks (DySAT), still have significant barriers to scalability, computational efficiency, and temporal embedding drift. To overcome these drawbacks, this paper presents a Topology-Aware Graph Neural Network (TAGNN) model capable of incorporating graph-topology encoding and the spatiotemporal memory mechanisms into the learning process of dynamic node representation. The proposed framework includes three key components: (i) returns global and local structural data such as graph Laplacian-based feature transformations and position encodings, (ii) models temporal relationships using Gated Recurrent Units (GRUs) with a self-attention mechanism, and (iii) reduces computational expense through neighbor sampling, hierarchical clustering, and mini-batches. Experimental findings on massive real-world dynamic data, such as social networks, traffic flow networks, and biological systems, indicate that TAGNN, with 92.4% node-classification accuracy, a 32-fold reduction in training time, and the ability to process up to 1 million edges, achieves excellent performance. It also achieves a temporal stability improvement of approximately 9.1% and outperforms TGN, EvolveGCN, and DySAT. The implications of these results are that topology-conscious encoding in combination with spatiotemporal memory capacities give more consistent and scalable representations of dynamic graphs. The implications of the proposed TAGNN framework on such applications as anomaly detection, social media analytics, and traffic forecasting are that the proposed framework provides an end-to-end solution that promotes accuracy, efficiency, and temporal consistency in dynamic graph learning.
