Graph generative models become increasingly effective for data distribution
approximation and data augmentation. While they have aroused public concerns
about their malicious misuses or misinformation broadcasts, just as what
Deepfake visual and auditory media has been delivering to society. Hence it is
essential to regulate the prevalence of generated graphs. To tackle this
problem, we pioneer the formulation of the generated graph detection problem to
distinguish generated graphs from real ones. We propose the first framework to
systematically investigate a set of sophisticated models and their performance
in four classification scenarios. Each scenario switches between seen and
unseen datasets/generators during testing to get closer to real-world settings
and progressively challenge the classifiers. Extensive experiments evidence
that all the models are qualified for generated graph detection, with specific
models having advantages in specific scenarios. Resulting from the validated
generality and oblivion of the classifiers to unseen datasets/generators, we
draw a safe conclusion that our solution can sustain for a decent while to curb
generated graph misuses.

我们提出了第一个生成图检测框架，通过四种分类场景探索了一系列复杂模型的性能，证明了这些模型都能够成功检测到生成的图，并且具有一定的稳健性和普适性，从而能够有效地遏制生成图的恶意滥用与虚假传播。