Dynamic Link Prediction Analysis Using Temporal Adaptive Graph Convolutional Long Short-Term Memory

The understanding of time dependent dynamics in evolving network interactions is crucial for applications ranging across various domains. In this paper, we introduce TA-GC-LSTM (Temporal Adaptive Graph Convolutional Long Short-Term Memory) which uses deep learning framework with novel combination of models. This proposed model of ours, efficiently captures spatial dependencies through graph convolution, temporal sequences using LSTM, and gives selective importance to influential time steps through the attention mechanism. In contravention to traditional methods, which rely on static graph representations, TA-GC-LSTM dynamically learns the temporal evolution of node relationships, enhancing predictive accuracy in link prediction tasks. In our framework, we have carried out processing of datasets by binning interactions into fixed time windows, encoding unique nodes with learnable embeddings, and filtering sparse time steps to optimize computational efficiency. To validate our approach, we have tested the model on three real-world datasets and compared our model performance against Graph Convolution Embedded LSTM (GC-LSTM) and Temporal Graph Convolutional Network (T-GCN) as benchmarks across multiple evaluation metrics. Our results demonstrated that TA-GC-LSTM outperforms baseline models, achieving an AUC score of 93%, while maintaining computational efficiency, making it a robust solution for modelling evolving graph structures.