1 code implementation • 24 May 2024 • Yihe Wang, Nan Huang, Taida Li, Yujun Yan, Xiang Zhang
Medical time series data, such as Electroencephalography (EEG) and Electrocardiography (ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases.
1 code implementation • 26 Jun 2023 • Gaotang Li, Marlena Duda, Xiang Zhang, Danai Koutra, Yujun Yan
Based on these insights, we propose a new model, Interpretable Graph Sparsification (IGS), which enhances graph classification performance by up to 5. 1% with 55. 0% fewer edges.
no code implementations • 24 May 2023 • Gaotang Li, Danai Koutra, Yujun Yan
Our empirical results reveal that our proposed size-insensitive attention strategy substantially enhances graph classification performance on large test graphs, which are 2-10 times larger than the training graphs, resulting in an improvement in F1 scores by up to 8%.
no code implementations • 1 May 2023 • Haohui Wang, Yuzhen Mao, Yujun Yan, Yaoqing Yang, Jianhui Sun, Kevin Choi, Balaji Veeramani, Alison Hu, Edward Bowen, Tyler Cody, Dawei Zhou
To answer it, we propose a generalization bound for dynamic non-IID transfer learning on graphs, which implies the generalization performance is dominated by domain evolution and domain discrepancy between source and target graphs.
1 code implementation • 30 Nov 2021 • Yefan Zhou, Yiru Shen, Yujun Yan, Chen Feng, Yaoqing Yang
Our finding shows that a leading factor in determining recognition versus reconstruction is how dispersed the training data is.
no code implementations • 5 Nov 2021 • Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, Danai Koutra
Unsupervised graph representation learning is critical to a wide range of applications where labels may be scarce or expensive to procure.
1 code implementation • 12 Feb 2021 • Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra
We are the first to take a unified perspective to jointly explain the oversmoothing and heterophily problems at the node level.
1 code implementation • 26 Aug 2020 • Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E. Gonzalez, Michael W. Mahoney
Replacing BN with the recently-proposed Group Normalization (GN) can reduce gradient diversity and improve test accuracy.
4 code implementations • NeurIPS 2020 • Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra
We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i. e., in networks where connected nodes may have different class labels and dissimilar features.
Graph Neural Network Node Classification on Non-Homophilic (Heterophilic) Graphs
1 code implementation • NeurIPS 2020 • Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
A significant effort has been made to train neural networks that replicate algorithmic reasoning, but they often fail to learn the abstract concepts underlying these algorithms.
no code implementations • ICLR 2020 • Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence.