19 citations · 25 across the 6 of their papers we have counts for
6 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…
Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions
Guneykan Ozgul, Xiantao Li, Mehrdad Mahdavi +1
We present quantum algorithms for sampling from non-logconcave probability distributions in the form of . Here, can be written as a finite sum $f(x):…
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…