Dynamic Graph Convolutional Networks
This addresses the challenge of handling dynamic graph data for classification tasks, which is an incremental advancement in graph neural networks.
The paper tackles the problem of classifying dynamic graphs, where vertices and edges change over time, by proposing two novel neural network approaches that combine Long Short-Term Memory networks and Graph Convolutional Networks to jointly exploit structured data and temporal information, achieving promising results.
Many different classification tasks need to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change during time. Our goal is to jointly exploit structured data and temporal information through the use of a neural network model. To the best of our knowledge, this task has not been addressed using these kind of architectures. For this reason, we propose two novel approaches, which combine Long Short-Term Memory networks and Graph Convolutional Networks to learn long short-term dependencies together with graph structure. The quality of our methods is confirmed by the promising results achieved.