Feature-Enhanced Graph Neural Networks for Classification of Synthetic Graph Generative Models: A Benchmarking Study

2025-12-20

AI Summary

This study addresses the automatic identification of synthetic graph generation models. We propose a hybrid classification framework integrating interpretable graph-theoretic features with graph neural networks (GNNs). On a large-scale heterogeneous graph dataset spanning five canonical generative models, we systematically co-optimize engineered topological features—both node- and graph-level—with six mainstream GNNs (GCN, GAT, GIN, GraphSAGE, GTN, etc.), revealing the critical role of message-passing mechanisms in discriminative performance. We incorporate ensemble random forest-based feature selection and Optuna-driven hyperparameter optimization, and establish the first benchmark evaluation suite for graph generation model classification. Experiments show GraphSAGE and GTN achieve 98.5% accuracy; t-SNE and UMAP visualizations demonstrate clear inter-class separation; GAT-based models underperform due to limitations in global structural modeling; and SVM baselines confirm the necessity of message passing for effective classification.

Technology Category

Machine Learning: Graph-based Machine LearningNatural Language Processing: GenerationData Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSocial Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• Classify synthetic graph generative models using GNNs and engineered features
• Integrate interpretable graph-theoretic features into six GNN architectures
• Benchmark hybrid models against SVM baseline for classification accuracy

Innovation

• Hybrid GNNs with engineered graph-theoretic features
• Random Forest based feature selection pipeline
• Six GNN architectures optimized with Optuna