DGNN-MN: Dynamic Graph Neural Network via memory regenerate and neighbor propagation

Dynamic Graph Neural Network (DGNN) models have been widely used for modelling, prediction and recommendation tasks in domains such as e-commerce and social networks, due to their ability to capture node interaction features and temporal features. Current methods for dynamic graph representation learning mainly depend on querying K-hop neighbors and the triadic closure law to derive node representations. However, as the number of layers in neural networks increases, this can cause problems with over-smoothing and overly complex calculations. Additionally, current models cannot ensure that events arrive at adjacent nodes in chronological order according to timestamps. To address these problems, we propose a Dynamic Graph Neural Network via Memory Regenerate and Neighbor Propagation(DGNN-MN) model. The model presents a memory regeneration strategy for obtaining node time information features and a time edge-propagating method for obtaining neighbour information. By combining these two methods to fuse output vectors, it captures node feature representations. In addition, we present a strategy for the timestamp encoding of node messages, which effectively ensures that node messages propagate to neighboring nodes in an ordered manner according to timestamps, thereby better capturing the temporal characteristics of events in dynamic graphs. Extensive experiments conducted on five public datasets demonstrate the effectiveness of DGNN-MN for link prediction and node classification task. Furthermore, the method outperforms other state-of-the-art methods. The data and code are available on GitHub.