A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Make Heterophilic Graphs Better Fit GNN: A Graph Rewiring Approach
Graph Neural Networks (GNNs) have shown superior performance in modeling graph data.
Existing studies have shown that a lot of GNNs perform well on homophilic graphs while …
Existing studies have shown that a lot of GNNs perform well on homophilic graphs while …
No change, no gain: empowering graph neural networks with expected model change maximization for active learning
Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …
graph-structured data, but their success depends on sufficient labeled data. We present a …
Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification
Graph neural networks (GNNs) have advanced the state of the art in various domains.
Despite their remarkable success, the uncertainty estimation of GNN predictions remains …
Despite their remarkable success, the uncertainty estimation of GNN predictions remains …
AI meets physics: a comprehensive survey
L Jiao, X Song, C You, X Liu, L Li, P Chen… - Artificial Intelligence …, 2024 - Springer
Uncovering the mechanisms of physics is driving a new paradigm in artificial intelligence
(AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide …
(AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide …
Conformalized Link Prediction on Graph Neural Networks
Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes
domains are often hampered by unreliable predictions. Although numerous uncertainty …
domains are often hampered by unreliable predictions. Although numerous uncertainty …
Uncertainty in Graph Neural Networks: A Survey
Graph Neural Networks (GNNs) have been extensively used in various real-world
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …
applications. However, the predictive uncertainty of GNNs stemming from diverse sources …
Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision
Graph Anomaly Detection (GAD) has surfaced as a significant field of research,
predominantly due to its substantial influence in production environments. Although existing …
predominantly due to its substantial influence in production environments. Although existing …
Graph Convolutional Network With Connectivity Uncertainty for EEG-Based Emotion Recognition
Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds
great potential in advancing human-computer interaction. However, several significant …
great potential in advancing human-computer interaction. However, several significant …
Free energy of bayesian convolutional neural network with skip connection
S Nagayasu, S Watanabe - Asian Conference on Machine …, 2024 - proceedings.mlr.press
Since the success of Residual Network (ResNet), many of architectures of Convolutional
Neural Networks (CNNs) have adopted skip connection. While the generalization …
Neural Networks (CNNs) have adopted skip connection. While the generalization …