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Towards Model Extraction Attacks in GAN-based Image Translation via Domain Shift Mitigation
Model extraction attacks (MEAs) enable an attacker to replicate the functionality of a victim deep neural network (DNN) model by only …
A Comprehensive Survey on Distributed Training of Graph Neural Networks
Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their …
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Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection
Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, …
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Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification
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Towards Flexible and Adaptive Neural Process for Cold-Start Recommendation
Recommender systems have been widely adopted in various online personal e-commerce applications for improving user experience. A …
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A Survey on Fairness-aware Recommender Systems
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and …
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A Survey on Neural-symbolic Learning Systems
In recent years, neural systems have demonstrated highly effective learning ability and superior perception intelligence. However, they …
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Boosting Graph Contrastive Learning via Adaptive Sampling
Contrastive Learning (CL) is a prominent technique for self-supervised representation learning, which aims to contrast semantically …
Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting
Recent studies focus on formulating the traffic forecasting as a spatio-temporal graph modeling problem. They typically construct a …
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Contrastive Graph Similarity Networks
Graph similarity learning is a significant and fundamental issue in the theory and analysis of graphs, which has been applied in a …