Search Results for author: Qingqing Long

Found 10 papers, 3 papers with code

COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing

no code implementations26 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.

Attribute Contrastive Learning +1

Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective

no code implementations21 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.

General Knowledge Graph Classification

A Survey of Data-Efficient Graph Learning

no code implementations1 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.

Graph Learning

A Comprehensive Survey on Deep Graph Representation Learning

no code implementations11 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.

Graph Embedding Graph Representation Learning

Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting

1 code implementation24 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.

Traffic Prediction

Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels

1 code implementation7 Apr 2021 Qingqing Long, Yilun Jin, Yi Wu, Guojie Song

However, the inability of GNNs to model substructures in graphs remains a significant drawback.

Graph Mining

Learning Node Representations from Noisy Graph Structures

no code implementations4 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.

Graph Reconstruction Node Classification

Graph Structural-topic Neural Network

1 code implementation25 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.

Topic Models

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