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02/22/2023
Do We Really Need Complicated Model Architectures For Temporal Networks?
Recurrent neural network (RNN) and self-attention mechanism (SAM) are th...
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02/17/2023
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection
As privacy protection receives much attention, unlearning the effect of ...
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11/19/2021
Dynamic Graph Representation Learning via Graph Transformer Networks
Dynamic graph representation learning is an important task with widespre...
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11/16/2021
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
Despite the recent success of Graph Neural Networks (GNNs), training GNN...
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10/28/2021
On Provable Benefits of Depth in Training Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are known to suffer from performance...
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03/03/2021
On the Importance of Sampling in Learning Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have achieved impressive empirical a...
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06/24/2020
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks
Sampling methods (e.g., node-wise, layer-wise, or subgraph) has become a...
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11/20/2018
Scene Graph Generation via Conditional Random Fields
Despite the great success object detection and segmentation models have ...
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07/28/2017