Search Results for author:
Found 8 papers, 2 papers with code
FRGNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction
To address this challenge, we propose FR-GNN, a general framework for GNNs to conduct feature reconstruction.
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
We introduce the key notion of label non-uniformity, which is derived from the Wasserstein distance between the softmax distribution of the logits and the uniform distribution.
Distributional Signals for Node Classification in Graph Neural Networks
In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP).
Policy Dispersion in Non-Markovian Environment
To resolve this problem, this paper tries to learn the diverse policies from the history of state-action pairs under a non-Markovian environment, in which a policy dispersion scheme is designed for seeking diverse policy representation.
Learning the Network of Graphs for Graph Neural Networks
Our model solves the first issue by simultaneously learning multiple relation graphs of data samples as well as a relation network of graphs, and solves the second and the third issue by selecting important data features as well as important data sample relations.
An Unsupervised Bayesian Neural Network for Truth Discovery in Social Networks
An autoencoder learns the complex relationships between event truths, agent reliabilities and agent observations.
GFCN: A New Graph Convolutional Network Based on Parallel Flows
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-structured data.
Using Social Network Information in Bayesian Truth Discovery
We incorporate knowledge of the agents' social network in our truth discovery framework and develop Laplace variational inference methods to estimate agents' reliabilities, communities, and the event states.
