Abstract:
The links in many real networks are evolving with time. The task of dynamic link prediction is to use past connection histories to infer links of the network at a future ...Show MoreMetadata
Abstract:
The links in many real networks are evolving with time. The task of dynamic link prediction is to use past connection histories to infer links of the network at a future time. How to effectively learn the temporal and structural pattern of the network dynamics is the key. In this paper, we propose a graph representation learning model based on enhanced structure and temporal information (GRL_EnSAT). For structural information, we exploit a combination of a graph attention network (GAT) and a self-attention network to capture structural neighborhood. For temporal dynamics, we use a masked self-attention network to capture the dynamics in the link evolution. In this way, GRL_EnSAT not only learns low-dimensional embedding vectors but also preserves the nonlinear dynamic feature of the evolving network. GRL_EnSAT is evaluated on four real datasets, in which GRL_EnSAT outperforms most advanced baselines. Benefiting from the dynamic self-attention mechanism, GRL_EnSAT yields better performance than approaches based on recursive graph evolution modeling.
Published in: 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Date of Conference: 24-26 May 2023
Date Added to IEEE Xplore: 22 June 2023
ISBN Information: