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Trustworthy graph learning: Reliability, explainability, and privacy protection
Deep graph learning (DGL) has achieved remarkable progress in both business and
scientific areas ranging from finance and e-commerce, to drug and advanced material …
scientific areas ranging from finance and e-commerce, to drug and advanced material …
Towards data-centric graph machine learning: Review and outlook
Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …
to drive AI models and applications, has attracted increasing attention in recent years. In this …
Federated graph learning under domain shift with generalizable prototypes
Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …
shared model on graph-structured data in the distributed environment. However, in real …
Neighbor contrastive learning on learnable graph augmentation
Recent years, graph contrastive learning (GCL), which aims to learn representations from
unlabeled graphs, has made great progress. However, the existing GCL methods mostly …
unlabeled graphs, has made great progress. However, the existing GCL methods mostly …
Subgraph federated learning with missing neighbor generation
Graphs have been widely used in data mining and machine learning due to their unique
representation of real-world objects and their interactions. As graphs are getting bigger and …
representation of real-world objects and their interactions. As graphs are getting bigger and …
Structural re-weighting improves graph domain adaptation
In many real-world applications, graph-structured data used for training and testing have
differences in distribution, such as in high energy physics (HEP) where simulation data used …
differences in distribution, such as in high energy physics (HEP) where simulation data used …
Hope: High-order graph ode for modeling interacting dynamics
Leading graph ordinary differential equation (ODE) models have offered generalized
strategies to model interacting multi-agent dynamical systems in a data-driven approach …
strategies to model interacting multi-agent dynamical systems in a data-driven approach …
Shift-robust gnns: Overcoming the limitations of localized graph training data
There has been a recent surge of interest in designing Graph Neural Networks (GNNs) for
semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled …
semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled …
Few-shot network anomaly detection via cross-network meta-learning
Network anomaly detection, also known as graph anomaly detection, aims to find network
elements (eg, nodes, edges, subgraphs) with significantly different behaviors from the vast …
elements (eg, nodes, edges, subgraphs) with significantly different behaviors from the vast …
Adaptive trajectory prediction via transferable gnn
Pedestrian trajectory prediction is an essential component in a wide range of AI applications
such as autonomous driving and robotics. Existing methods usually assume the training and …
such as autonomous driving and robotics. Existing methods usually assume the training and …