6 citations · 24 across the 9 of their papers we have counts for
10 papers
Adversarial Collaborative Filtering for Free
Huiyuan Chen, Xiaoting Li, Vivian Lai +5
Collaborative Filtering (CF) has been successfully used to help users discover the items of interest. Nevertheless, existing CF methods suffer from noisy data issue, which negative…
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding
Yan Zheng, Junpeng Wang, Chin-Chia Michael Yeh +4
Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance re…
Sharpness-Aware Graph Collaborative Filtering
Huiyuan Chen, Chin-Chia Michael Yeh, Yujie Fan +5
Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, GNNs tend to yield inferior performance when the distributions of training an…
PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs
Xin Dai, Yujie Fan, Zhongfang Zhuang +7
Pre-training on large models is prevalent and emerging with the ever-growing user-generated content in many machine learning application categories. It has been recognized that lea…
Dynamic Graph Node Classification via Time Augmentation
Jiarui Sun, Mengting Gu, Chin-Chia Michael Yeh +3
Node classification for graph-structured data aims to classify nodes whose labels are unknown. While studies on static graphs are prevalent, few studies have focused on dynamic gra…
Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning
Mingxuan Ju, Yujie Fan, Chuxu Zhang +1
Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security sta…
Boosting Graph Neural Networks via Adaptive Knowledge Distillation
Zhichun Guo, Chunhui Zhang, Yujie Fan +3
Graph neural networks (GNNs) have shown remarkable performance on diverse graph mining tasks. Although different GNNs can be unified as the same message passing framework, they lea…
Black-box Node Injection Attack for Graph Neural Networks
Mingxuan Ju, Yujie Fan, Yanfang Ye +1
Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to vital fields that require high security standard such as product recommend…
Adaptive Kernel Graph Neural Network
Mingxuan Ju, Shifu Hou, Yujie Fan +3
Graph neural networks (GNNs) have demonstrated great success in representation learning for graph-structured data. The layer-wise graph convolution in GNNs is shown to be powerful…
Heterogeneous Temporal Graph Neural Network
Yujie Fan, Mingxuan Ju, Chuxu Zhang +2
Graph neural networks (GNNs) have been broadly studied on dynamic graphs for their representation learning, majority of which focus on graphs with homogeneous structures in the spa…