# SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking

arXiv: https://arxiv.org/abs/2609.10742 · HTML: https://mlsanity.com/paper/2609.10742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 9 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [Scale-free networks are rare](https://mlsanity.com/paper/1801.03400/) (2018)
- [Reliable Node Similarity Matrix Guided Contrastive Graph Clustering](https://mlsanity.com/paper/2408.03765/) (2024)
- [GenCAT: Generating Attributed Graphs with Controlled Relationships between Classes, Attributes, and Topology](https://mlsanity.com/paper/2109.04639/) (2021)
- [Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering](https://mlsanity.com/paper/2602.08519/) (2026)

## Abstract (arXiv)

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. Nevertheless, most existing generators rely heavily on power-law degree distributions, despite recent evidence indicating that scale-free networks are rare, particularly in social network contexts. Moreover, state-of-the-art attributed graph generators provide limited flexibility, as they do not allow users to construct communities with varying densities, degree distributions, and sub-community structures. To overcome these limitations, we introduce the Synthetic Community-Aware Attributed Graph Generator (SynCo), a graph generation algorithm that allows users to control the node degree distribution and sub-community structure. We evaluate SynCo across three different tasks: graph mimicking, hyperparameter evaluation, and node clustering tuning. The results show that our model outperforms state-of-the-art approaches in synthetic graph generation and data augmentation, while preserving the original distributions of duplicated and augmented datasets, as confirmed by statistical tests well know in literature. We also demonstrate the ability of SynCo to generate nodes in large scale, up to 2.1 million nodes.