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09/18/2023
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
Federated training of Graph Neural Networks (GNN) has become popular in ...
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06/03/2023
Scaling Up, Scaling Deep: Blockwise Graph Contrastive Learning
Oversmoothing is a common phenomenon in graph neural networks (GNNs), in...
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05/30/2023
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
While contrastive self-supervised learning has become the de-facto learn...
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05/18/2023
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
The prevalence of large-scale graphs poses great challenges in time and ...
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04/06/2023
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
Knowledge Graph Embedding (KGE) is a fundamental technique that extracts...
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03/18/2023
Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions
We present neural frailty machine (NFM), a powerful and flexible neural ...
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03/06/2023
DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk
Graph representation plays an important role in the field of financial r...
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02/04/2023
GRANDE: a neural model over directed multigraphs with application to anti-money laundering
The application of graph representation learning techniques to the area ...
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10/10/2022
Long N-step Surrogate Stage Reward to Reduce Variances of Deep Reinforcement Learning in Complex Problems
High variances in reinforcement learning have shown impeding successful ...
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08/15/2022
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
Recent years have seen a surge in research on dynamic graph representati...
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06/21/2022
sqSGD: Locally Private and Communication Efficient Federated Learning
Federated learning (FL) is a technique that trains machine learning mode...
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05/20/2022
MaskGAE: Masked Graph Modeling Meets Graph Autoencoders
We present masked graph autoencoder (MaskGAE), a self-supervised learnin...
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04/20/2022
GUARD: Graph Universal Adversarial Defense
Recently, graph convolutional networks (GCNs) have shown to be vulnerabl...
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07/03/2021
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Deep learning provides a promising way to extract effective representati...
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01/22/2021
Robotic Knee Tracking Control to Mimic the Intact Human Knee Profile Based on Actor-critic Reinforcement Learning
We address a state-of-the-art reinforcement learning (RL) control approa...
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01/10/2021