2 citations · 6 across the 8 of their papers we have counts for
8 papers
FRGNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction
Rui Ding, Jielong Yang, Feng Ji +2
Due to inappropriate sample selection and limited training data, a distribution shift often exists between the training and test sets. This shift can adversely affect the test perf…
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
Feng Ji, See Hian Lee, Hanyang Meng +3
In node classification using graph neural networks (GNNs), a typical model generates logits for different class labels at each node. A softmax layer often outputs a label predictio…
Distributional Signals for Node Classification in Graph Neural Networks
Feng Ji, See Hian Lee, Kai Zhao +2
In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP). While it is common in GSP to impose sig…
Policy Dispersion in Non-Markovian Environment
Bohao Qu, Xiaofeng Cao, Jielong Yang +4
Markov Decision Process (MDP) presents a mathematical framework to formulate the learning processes of agents in reinforcement learning. MDP is limited by the Markovian assumption…
Learning the Network of Graphs for Graph Neural Networks
Yixiang Shan, Jielong Yang, Xing Liu +3
Graph neural networks (GNNs) have achieved great success in many scenarios with graph-structured data. However, in many real applications, there are three issues when applying GNNs…
An Unsupervised Bayesian Neural Network for Truth Discovery in Social Networks
Jielong Yang, Wee Peng Tay
The problem of estimating event truths from conflicting agent opinions in a social network is investigated. An autoencoder learns the complex relationships between event truths, ag…
GFCN: A New Graph Convolutional Network Based on Parallel Flows
Feng Ji, Jielong Yang, Qiang Zhang +1
In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph…
Using Social Network Information in Bayesian Truth Discovery
Jielong Yang, Junshan Wang, Wee Peng Tay
We investigate the problem of truth discovery based on opinions from multiple agents who may be unreliable or biased. We consider the case where agents' reliabilities or biases are…