Generated Graph Detection
Yihan Ma Zhikun Zhang Ning Yu Xinlei He Michael Backes Yun Shen Yang Zhang
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.