1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 1 cited
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…
cs.SI2020
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…
cs.LG2020
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…