SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking
Graph Neural Networks (GNNs) are powerful models for handling attributed graphs in tasks such as classification, link prediction, and community detection, as they enable the aggregation of information from both structural and semantic sources. However, progress in community detection is hindered by the lack of high-quality datasets, since ground-truth community labels are often unavailable and most algorithms proposed in recent literature rely on the same benchmark datasets for model training and evaluation. To address this issue, attributed random graph generators are commonly employed to create synthetic graphs for assessing the strengths and limitations of GNN-based models. Nevertheles...
| Authors | Guilherme Henrique Messias, Mariana Caravanti de Souza, Sylvia Iasulaitis et al. |
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| Primary category | cs.SI |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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