Dual-Track Interactive-Fusion Directed Graph Neural Network for Entity Identification in Information Propagation

This study addresses the entity identification problem in information propagation networks and proposes a novel dual-track interactive-fusion directed graph neural network (GNN). The method characterizes node features from two complementary perspectives: static topology and dynamic path dependency. On the one hand, it considers the relative position of nodes in directed network path sequences and enhances the model's ability to represent directed structural relations through static-track encoding. On the other hand, dynamic-track encoding is used to learn the sequential associations among nodes in network path sequences. Based on this, a bidirectional cross-track interactive fusion mechanism is introduced to facilitate the interaction, fusion, and collaborative enhancement of latent information between the static and dynamic tracks, thereby improving node representations in directed graphs. Experiments on the Cora, Reddit, PubMed, and Ogbn-arxiv datasets show that, compared with the best baseline, the proposed dual-track interactive-fusion method improves the F1-score by 1.83%, 3.32%, 3.19%, and 2.61%, respectively. The corresponding reduction rates in the remaining performance gap are 5.93%, 11.98%, 13.98%, and 8.55%, respectively. This study provides a new theoretical perspective and technical approach for handling complex dynamic directed graphs and has important application value in social media analysis, public opinion monitoring, and network security.