Peng Yue, Xia Jiwen, Liu Dafeng, Liu Miao, Xiao Long, Shi Benyun
College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211800, China.
College of Artificial Intelligence, Nanjing Tech University, Nanjing, 211800, China.
Sci Rep. 2025 Jan 4;15(1):805. doi: 10.1038/s41598-024-84816-z.
Graph data is essential for modeling complex relationships among entities. Graph Neural Networks (GNNs) have demonstrated effectiveness in processing low-order undirected graph data; however, in complex directed graphs, relationships between nodes extend beyond first-order connections and encompass higher-order relationships. Additionally, the asymmetry introduced by edge directionality further complicates node interactions, presenting greater challenges for extracting node information. In this paper, We propose TWC-GNN, a novel graph neural network design, as a solution to this problem. TWC-GNN uses node degrees to define higher-order topological structures, assess node importance, and capture mutual interactions between central nodes and their adjacent counterparts. This approach improves our understanding of complex relationships within the network. Furthermore, by integrating self-attention mechanisms, TWC-GNN effectively gathers higher-order node information in addition to focusing on first-order node information. Experimental results demonstrate that the integration of topological structures and higher-order node information is crucial for the learning process of graph neural networks, particularly in directed graphs, leading to improved classification accuracy.
图数据对于建模实体之间的复杂关系至关重要。图神经网络（GNN）已在处理低阶无向图数据方面展现出有效性；然而，在复杂有向图中，节点之间的关系不仅限于一阶连接，还包括高阶关系。此外，边的方向性所引入的不对称性进一步使节点交互变得复杂，给提取节点信息带来了更大挑战。在本文中，我们提出了TWC-GNN，一种新颖的图神经网络设计，作为解决此问题的方案。TWC-GNN使用节点度数来定义高阶拓扑结构、评估节点重要性，并捕捉中心节点与其相邻节点之间的相互作用。这种方法增进了我们对网络内复杂关系的理解。此外，通过整合自注意力机制，TWC-GNN除了关注一阶节点信息外，还能有效地收集高阶节点信息。实验结果表明，拓扑结构和高阶节点信息的整合对于图神经网络的学习过程至关重要，特别是在有向图中，可提高分类准确率。
