no code implementations • 9 Nov 2023 • Jialin Chen, Kenza Amara, Junchi Yu, Rex Ying
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks.
1 code implementation • 6 Oct 2023 • Junchi Yu, Ran He, Rex Ying
These analogous problems are related to the input one, with reusable solutions and problem-solving strategies.
1 code implementation • 18 Jul 2023 • Kaiwei Zhang, Junchi Yu, Haichao Shi, Jian Liang, Xiao-Yu Zhang
Our intuition is to exploit the diverse counterfactual evidence of an event graph to serve as multi-view interpretations, which are further aggregated for robust rumor detection results.
1 code implementation • CVPR 2023 • Junchi Yu, Jian Liang, Ran He
Recent works employ different graph editions to generate augmented environments and learn an invariant GNN for generalization.
no code implementations • 27 Oct 2022 • Jie Cao, Mandi Luo, Junchi Yu, Ming-Hsuan Yang, Ran He
Then, we optimize the augmented samples by minimizing the norms of the data scores, i. e., the gradients of the log-density functions.
no code implementations • 19 Jun 2022 • Junchi Yu, Jian Liang, Ran He
Extensive experiments on both node-level and graph-level benchmarks shows that the proposed DPS achieves impressive performance for various graph domain generalization tasks.
no code implementations • 20 May 2022 • Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
no code implementations • 18 Dec 2021 • Junchi Yu, Tingyang Xu, Ran He
In this work, we address these key challenges and propose IFEXPLAINER, which generates a necessary and sufficient explanation for GNNs.
1 code implementation • CVPR 2022 • Junchi Yu, Jie Cao, Ran He
Subgraph recognition aims at discovering a compressed substructure of a graph that is most informative to the graph property.
no code implementations • 20 Mar 2021 • Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He
The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning.
1 code implementation • ICLR 2021 • Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, Ran He
In this paper, we propose a framework of Graph Information Bottleneck (GIB) for the subgraph recognition problem in deep graph learning.
no code implementations • 20 Apr 2020 • Yi Li, Huaibo Huang, Junchi Yu, Ran He, Tieniu Tan
Face verification aims at determining whether a pair of face images belongs to the same identity.