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National Engineering Research Center for Visual Information and Applications, Xi'an Jiaotong University
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Unsupervised graph anomaly detection is now possible across diverse graphs, achieving zero-shot generalization without relying on labeled data or few-shot examples.
Uncover tax evasion rings with a novel graph neural network that leverages related party transaction data to significantly outperform existing detection methods.
Forget complex sequence models: this new method efficiently captures temporal dynamics in graphs by contrasting node representations across different timespans.
