DAGCN: A Novel Directed Aggregation Graph Convolutional Network for Optimizing Receptive Field Expansion

Graph Neural Networks (GNNs) have achieved notable success in graph-structured data tasks, such as social networks, recommendation systems, molecular structures, and citation networks. By leveraging a message-passing framework, GNNs capture node dependencies and demonstrate strong performance across diverse tasks. However, most existing GNN models neglect edge directionality in directed graphs, limiting their ability to accurately model information propagation and node dependencies. To this end, this paper proposes the Directed Aggregation Graph Convolutional Network (DAGCN), a novel approach that incorporates directional features from both first-order and second-order neighbors, effectively capturing directional information in directed graphs. DAGCN extends the receptive field with a single-layer aggregation mechanism, mitigating challenges caused by symmetry in traditional methods. Additionally, a parameterized weighting scheme dynamically adjusts the fusion of local and global information, enhancing the node representation and graph structure modeling accuracy. The flexible neighbor aggregation strategy further improves adaptability and robustness. Extensive experiments on several publicly available datasets show that DAGCN outperforms existing methods in capturing directional information and expanding the receptive field. On the CITESEER-FULL dataset, DAGCN achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$95.63 \pm 0.44\%$</tex-math></inline-formula> accuracy, surpassing GCN by 6.39 percentage points (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$89.24 \pm 0.75\%$</tex-math></inline-formula>). On the SQUIRREL dataset, DAGCN improves by 22.02 percentage points, reaching <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$75.87 \pm 2.16\%$</tex-math></inline-formula> compared to GCN's <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$53.85 \pm 2.21\%$</tex-math></inline-formula>. These results underscore the importance of incorporating directional information to enhance performance, especially in directed graph tasks.