91 citations · 572 across the 26 of their papers we have counts for
5 papers · 1 filter
Node Similarity Preserving Graph Convolutional Networks
Wei Jin, Tyler Derr, Yiqi Wang +3
Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the grap…
A Unified View on Graph Neural Networks as Graph Signal Denoising
Yao Ma, Xiaorui Liu, Tong Zhao +3
Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. A single GNN layer typically consists of a feature transformation and a…
Graph Structure Learning for Robust Graph Neural Networks
Wei Jin, Yao Ma, Xiaorui Liu +3
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, cal…
Customized Graph Neural Networks
Yiqi Wang, Yao Ma, Wei Jin +3
Recently, Graph Neural Networks (GNNs) have greatly advanced the task of graph classification. Typically, we first build a unified GNN model with graphs in a given training set and…
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
Wenqi Fan, Tyler Derr, Xiangyu Zhao +5
Recently, recommender systems that aim to suggest personalized lists of items for users to interact with online have drawn a lot of attention. In fact, many of these state-of-the-a…