623 citations · 961 across the 7 of their papers we have counts for
7 papers
FOCAL-Attention for Heterogeneous Multi-Label Prediction
Chenghao Zhang, Qingqing Long, Ludi Wang +3
Heterogeneous graphs have attracted increasing attention for modeling multi-typed entities and relations in complex real-world systems. Multi-label node classification on heterogen…
A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu +13
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…
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
Zheng Fang, Qingqing Long, Guojie Song +1
Spatial-temporal forecasting has attracted tremendous attention in a wide range of applications, and traffic flow prediction is a canonical and typical example. The complex and lon…
Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction
Feng Li, Bencheng Yan, Qingqing Long +4
Cross features play an important role in click-through rate (CTR) prediction. Most of the existing methods adopt a DNN-based model to capture the cross features in an implicit mann…
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
Qingqing Long, Yilun Jin, Yi Wu +1
Graph neural networks (GNNs) have achieved tremendous success in graph mining. However, the inability of GNNs to model substructures in graphs remains a significant drawback. Speci…
Learning Node Representations from Noisy Graph Structures
Junshan Wang, Ziyao Li, Qingqing Long +3
Learning low-dimensional representations on graphs has proved to be effective in various downstream tasks. However, noises prevail in real-world networks, which compromise networks…
Graph Structural-topic Neural Network
Qingqing Long, Yilun Jin, Guojie Song +2
Graph Convolutional Networks (GCNs) achieved tremendous success by effectively gathering local features for nodes. However, commonly do GCNs focus more on node features but less on…