用图神经网络+人工特征区分五类生成图模型，准确率达98.5%。
Feature-Enhanced Graph Neural Networks for Classification of Synthetic Graph Generative Models: A Benchmarking Study
- 融合图神经网络与手工提取的节点/图级特征进行分类。
- GraphSAGE和GTN达到98.5%准确率，显著优于GAT等模型。
- 结果验证了消息传递机制对区分生成图结构的关键作用。
区分生成图模型对理解合成图及现实结构中的复杂模式至关重要。尽管图神经网络（GNN）在图分类任务中表现优异，但很少有研究将其与可解释的图论特征结合。本文提出一种混合方法，结合GNN与工程化图论特征，对五类代表性生成图模型——Erdos-Renyi、Watts-Strogatz、Barabasi-Albert、Holme-Kim和随机块模型——进行分类。我们构建了一个包含多达1×10⁴个节点、1.1×10⁵条边的结构多样合成数据集。针对每张图提取丰富节点与图级特征，并通过基于随机森林的特征选择流程进行筛选。将特征融入六种GNN架构：GCN、GAT、GATv2、GIN、GraphSAGE和GTN，使用Optuna优化超参数。最终模型性能与仅依赖手工特征的SVM基线对比。结果显示，GraphSAGE与GTN达到最高准确率98.5%，且t-SNE与UMAP可视化显示强类别分离；而基于GAT的模型因难以捕捉全局结构表现较差。SVM基线证实消息传递机制对性能提升和有意义分类的重要性。
原文摘要 · Abstract (English)
The ability to discriminate between generative graph models is critical to understanding complex structural patterns in both synthetic graphs and the real-world structures that they emulate. While Graph Neural Networks (GNNs) have seen increasing use to great effect in graph classification tasks, few studies explore their integration with interpretable graph theoretic features. This paper investigates the classification of synthetic graph families using a hybrid approach that combines GNNs with engineered graph-theoretic features. We generate a large and structurally diverse synthetic dataset comprising graphs from five representative generative families, Erdos-Renyi, Watts-Strogatz, Barab'asi-Albert, Holme-Kim, and Stochastic Block Model. These graphs range in size up to 1x10^4 nodes, containing up to 1.1x10^5 edges. A comprehensive range of node and graph level features is extracted for each graph and pruned using a Random Forest based feature selection pipeline. The features are integrated into six GNN architectures: GCN, GAT, GATv2, GIN, GraphSAGE and GTN. Each architecture is optimised for hyperparameter selection using Optuna. Finally, models were compared against a baseline Support Vector Machine (SVM) trained solely on the handcrafted features. Our evaluation demonstrates that GraphSAGE and GTN achieve the highest classification performance, with 98.5% accuracy, and strong class separation evidenced by t-SNE and UMAP visualisations. GCN and GIN also performed well, while GAT-based models lagged due to limitations in their ability to capture global structures. The SVM baseline confirmed the importance of the message passing functionality for performance gains and meaningful class separation.
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