19 citations · 68 across the 11 of their papers we have counts for
11 papers
On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method
Weilin Cong, Jian Kang, Hanghang Tong +1
Temporal Graph Learning (TGL) has become a prevalent technique across diverse real-world applications, especially in domains where data can be represented as a graph and evolves ov…
BeMap: Balanced Message Passing for Fair Graph Neural Network
Xiao Lin, Jian Kang, Weilin Cong +1
Fairness in graph neural networks has been actively studied recently. However, existing works often do not explicitly consider the role of message passing in introducing or amplify…
Do We Really Need Complicated Model Architectures For Temporal Networks?
Weilin Cong, Si Zhang, Jian Kang +5
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning. Interestingly, we fo…
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection
Weilin Cong, Mehrdad Mahdavi
As privacy protection receives much attention, unlearning the effect of a specific node from a pre-trained graph learning model has become equally important. However, due to the no…
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability
Weilin Cong, Yanhong Wu, Yuandong Tian +4
Transformers have achieved great success in several domains, including Natural Language Processing and Computer Vision. However, its application to real-world graphs is less explor…
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi +2
Despite the recent success of Graph Neural Networks (GNNs), training GNNs on large graphs remains challenging. The limited resource capacities of the existing servers, the dependen…
On Provable Benefits of Depth in Training Graph Convolutional Networks
Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi
Graph Convolutional Networks (GCNs) are known to suffer from performance degradation as the number of layers increases, which is usually attributed to over-smoothing. Despite the a…
On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction
Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi
Graph Convolutional Networks (GCNs) have achieved impressive empirical advancement across a wide variety of semi-supervised node classification tasks. Despite their great success,…
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks
Weilin Cong, Rana Forsati, Mahmut Kandemir +1
Sampling methods (e.g., node-wise, layer-wise, or subgraph) has become an indispensable strategy to speed up training large-scale Graph Neural Networks (GNNs). However, existing sa…
Scene Graph Generation via Conditional Random Fields
Weilin Cong, William Wang, Wang-Chien Lee
Despite the great success object detection and segmentation models have achieved in recognizing individual objects in images, performance on cognitive tasks such as image caption,…
Improved Face Detection and Alignment using Cascade Deep Convolutional Network
Weilin Cong, Sanyuan Zhao, Hui Tian +1
Real-world face detection and alignment demand an advanced discriminative model to address challenges by pose, lighting and expression. Illuminated by the deep learning algorithm,…