对比传统方法与图神经网络在节点分类中的表现，发现后者提升43%至70%。
Research on the application of graph data structure and graph neural network in node classification/clustering tasks
- 结合经典图算法与图神经网络，提升节点分类效果。
- 实验显示图神经网络比传统方法准确率提升43%至70%。
- 适合对图数据建模和表示学习感兴趣的读者。
图结构数据广泛存在于社交网络、生物网络和知识图谱等领域。由于其非欧几里得特性，传统机器学习方法面临挑战。本研究系统分析图数据结构、经典图算法及图神经网络（GNNs），并进行理论剖析与对比评估。通过对比实验，定量分析传统算法与GNN在节点分类与聚类任务中的性能差异。结果表明，GNN在准确率上相较传统方法提升43%至70%。进一步探索经典算法与GNN架构的融合策略，为推进图表示学习研究提供理论指导。
原文摘要 · Abstract (English)
Graph-structured data are pervasive across domains including social networks, biological networks, and knowledge graphs. Due to their non-Euclidean nature, such data pose significant challenges to conventional machine learning methods. This study investigates graph data structures, classical graph algorithms, and Graph Neural Networks (GNNs), providing comprehensive theoretical analysis and comparative evaluation. Through comparative experiments, we quantitatively assess performance differences between traditional algorithms and GNNs in node classification and clustering tasks. Results show GNNs achieve substantial accuracy improvements of 43% to 70% over traditional methods. We further explore integration strategies between classical algorithms and GNN architectures, providing theoretical guidance for advancing graph representation learning research.
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