39 citations · 110 across the 7 of their papers we have counts for
7 papers
Contrastive Knowledge Graph Error Detection
Qinggang Zhang, Junnan Dong, Keyu Duan +3
Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are…
GPatch: Patching Graph Neural Networks for Cold-Start Recommendations
Hao Chen, Zefan Wang, Yue Xu +2
Cold start is an essential and persistent problem in recommender systems. State-of-the-art solutions rely on training hybrid models for both cold-start and existing users/items, ba…
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
Song Wang, Yushun Dong, Xiao Huang +2
Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose t…
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs
Qiaoyu Tan, Ninghao Liu, Xiao Huang +3
We introduce a novel masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data. Taking insights from self-supervised learning, we randomly mas…
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Kaixiong Zhou, Xiao Huang, Daochen Zha +4
Graph neural networks (GNNs) integrate deep architectures and topological structure modeling in an effective way. However, the performance of existing GNNs would decrease significa…
Dynamic Memory based Attention Network for Sequential Recommendation
Qiaoyu Tan, Jianwei Zhang, Ninghao Liu +4
Sequential recommendation has become increasingly essential in various online services. It aims to model the dynamic preferences of users from their historical interactions and pre…