The topic of synthetic graph generators (SGGs) has recently received much
attention due to the wave of the latest breakthroughs in generative modelling.
However, many state-of-the-art SGGs do not scale well with the graph size.
Indeed, in the generation process, all the possible edges for a fixed number of
nodes must often be considered, which scales in $\mathcal{O}(N^2)$, with $N$
being the number of nodes in the graph. For this reason, many state-of-the-art
SGGs are not applicable to large graphs. In this paper, we present SANGEA, a
sizeable synthetic graph generation framework which extends the applicability
of any SGG to large graphs. By first splitting the large graph into
communities, SANGEA trains one SGG per community, then links the community
graphs back together to create a synthetic large graph. Our experiments show
that the graphs generated by SANGEA have high similarity to the original graph,
in terms of both topology and node feature distribution. Additionally, these
generated graphs achieve high utility on downstream tasks such as link
prediction. Finally, we provide a privacy assessment of the generated graphs to
show that, even though they have excellent utility, they also achieve
reasonable privacy scores.

通过将大图分解成社区，并对每个社区使用一个合成图生成器进行训练，然后将社区图链接在一起以创建一个合成的大图，我们提出了一个大小可扩展的合成图生成框架 SANGEA，其扩展了任何合成图生成器对大图的适用性，并且通过我们的实验证明了 SANGEA 生成的图与原始图在拓扑结构和节点特征分布方面具有很高的相似性，同时在链接预测等下游任务上具有很高的效用，且对生成的图进行了隐私评估以证明在具有良好效用的同时，它们也达到了合理的隐私得分。

SANGEA：可扩展的和带属性的网络生成

SANGEA: Scalable and Attributed Network Generation

Despite a surge in interest in GNN development, homogeneity in benchmarking
datasets still presents a fundamental issue to GNN research. GraphWorld is a
recent solution which uses the Stochastic Block Model (SBM) to generate diverse
populations of synthetic graphs for benchmarking any GNN task. Despite its
success, the SBM imposed fundamental limitations on the kinds of graph
structure GraphWorld could create.
In this work we examine how two additional synthetic graph generators can
improve GraphWorld's evaluation; LFR, a well-established model in the graph
clustering literature and CABAM, a recent adaptation of the Barabasi-Albert
model tailored for GNN benchmarking. By integrating these generators, we
significantly expand the coverage of graph space within the GraphWorld
framework while preserving key graph properties observed in real-world
networks. To demonstrate their effectiveness, we generate 300,000 graphs to
benchmark 11 GNN models on a node classification task. We find GNN performance
variations in response to homophily, degree distribution and feature signal.
Based on these findings, we classify models by their sensitivity to the new
generators under these properties. Additionally, we release the extensions made
to GraphWorld on the GitHub repository, offering further evaluation of GNN
performance on new graphs.

通过整合多个综合性生成器，我们扩展了 GraphWorld 框架的图空间覆盖范围，并保留了真实网络中观察到的关键图属性，同时基于性质对模型进行分类，为 GNN 性能评估提供了更多图数据。