Dynamic Graph Neural Network with Motif Reconstruction

Zhiqiang Wang, Baijing Hu, Xingwang Zhao, Yajun Lin, Yuqi Wang

Abstract: Dynamic graph representation learning aims to generate low-dimensional latent vector representations of graphs or nodes at various time points from evolving graph datas, which are then used for downstream tasks like link prediction and node classification. Existing graph neural network-based approaches primarily focus on modeling node and edge interactions, but fail to capture the spatial structure of these interactions, limiting their ability to represent nodes at different time points within the dynamic graph. To overcome the limitations, we propose a novel dynamic graph neural network representation learning method based on motif reconstruction. The method begins with a dynamic motif sampling strategy that considers both temporal and spatial dimensions to capture the evolving interaction patterns of node neighborhoods. Next, a dynamic graph neural network model based on motif reconstruction is developed, using dynamic motif reconstruction to create a new computational graph for aggregating information from node neighborhoods. Additionally, we propose a motif information aggregation method based on neural ordinary differential equations, which enables the representation of nodes at any given time. We conduct comparative experiments on seven publicly available dynamic graph datasets, benchmarking our method against six state-of-the-art dynamic graph representation learning methods. The results demonstrate that our approach outperforms others in both dynamic link prediction and node classification tasks.

External IDs:dblp:conf/ijcnn/WangHZLW25