no code implementations • 20 May 2024 • Wei Ju, Yifan Wang, Yifang Qin, Zhengyang Mao, Zhiping Xiao, Junyu Luo, Junwei Yang, Yiyang Gu, Dongjie Wang, Qingqing Long, Siyu Yi, Xiao Luo, Ming Zhang
In recent years, deep learning on graphs has achieved remarkable success in various domains.
no code implementations • 26 Feb 2024 • Yihang Zhou, Qingqing Long, Yuchen Yan, Xiao Luo, Zeyu Dong, Xuezhi Wang, Zhen Meng, Pengfei Wang, Yuanchun Zhou
Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios.
no code implementations • 21 Feb 2024 • Yuchen Yan, Peiyan Zhang, Zheng Fang, Qingqing Long
Based on the insight of graph pre-training, we propose to bridge the graph signal gap and the graph structure gap with learnable prompts in the spectral space.
no code implementations • 2 Feb 2024 • Qingqing Long, Zheng Fang, Chen Fang, Chong Chen, Pengfei Wang, Yuanchun Zhou
To tackle the above issues, we propose a neural Spatial-Temporal Delay Differential Equation model, namely STDDE.
no code implementations • 1 Feb 2024 • Wei Ju, Siyu Yi, Yifan Wang, Qingqing Long, Junyu Luo, Zhiping Xiao, Ming Zhang
Graph-structured data, prevalent in domains ranging from social networks to biochemical analysis, serve as the foundation for diverse real-world systems.
no code implementations • 11 Apr 2023 • Wei Ju, Zheng Fang, Yiyang Gu, Zequn Liu, Qingqing Long, Ziyue Qiao, Yifang Qin, Jianhao Shen, Fang Sun, Zhiping Xiao, Junwei Yang, Jingyang Yuan, Yusheng Zhao, Yifan Wang, Xiao Luo, Ming Zhang
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.
1 code implementation • 24 Jun 2021 • Zheng Fang, Qingqing Long, Guojie Song, Kunqing Xie
However, the representation ability of such models is limited due to: (1) shallow GNNs are incapable to capture long-range spatial correlations, (2) only spatial connections are considered and a mass of semantic connections are ignored, which are of great importance for a comprehensive understanding of traffic networks.
Ranked #11 on Traffic Prediction on PeMS07
no code implementations • 17 May 2021 • Feng Li, Bencheng Yan, Qingqing Long, Pengjie Wang, Wei Lin, Jian Xu, Bo Zheng
Most of the existing methods adopt a DNN-based model to capture the cross features in an implicit manner.
1 code implementation • 7 Apr 2021 • Qingqing Long, Yilun Jin, Yi Wu, Guojie Song
However, the inability of GNNs to model substructures in graphs remains a significant drawback.
no code implementations • 4 Dec 2020 • Junshan Wang, Ziyao Li, Qingqing Long, Weiyu Zhang, Guojie Song, Chuan Shi
Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting.
1 code implementation • 25 Jun 2020 • Qingqing Long, Yilun Jin, Guojie Song, Yi Li, Wei. Lin
Specifically, we build topic models upon graphs using anonymous walks and Graph Anchor LDA, an LDA variant that selects significant structural patterns first, so as to alleviate the complexity and generate structural topics efficiently.