DynamicG2B: Dynamic Node Classification with Layered Graph Neural Networks and BiLSTM

Abstract

Most studies in graph theory assume that graphs are static, but in reality, graph structures and features change over time, leading to the concept of dynamic graphs, which is an under-researched area. Contemporary research in dynamic graph representation learning typically treats different snapshots of the graph as separate entities, disregarding the benefits of incorporating temporal information. While some techniques try to solve this problem using recurrent neural network-based solutions, these approaches still face the challenge of the vanishing or exploding gradient problem and complicated training procedures. To address these issues, we propose DynamicG2B, a BiLSTM-based graph neural architecture that computes node representations guided by attention using neighborhood aggregation. Our method applies relevant attention weights at different time steps to classify nodes in a supervised manner, utilizing dynamic edges and node feature information. Our evaluation of two benchmark datasets shows that DynamicG2B outperforms seven state-of-the-art baseline models in node classification in dynamic graphs. Additionally, our analysis of attention weights opens up opportunities for further research into exploring the importance of relationships among graph nodes.