Private graph data release: A survey
The application of graph analytics to various domains has yielded tremendous societal and
economical benefits in recent years. However, the increasingly widespread adoption of …
economical benefits in recent years. However, the increasingly widespread adoption of …
Higher order fractal belief Rényi divergence with its applications in pattern classification
Information can be quantified and expressed by uncertainty, and improving the decision
level of uncertain information is vital in modeling and processing uncertain information …
level of uncertain information is vital in modeling and processing uncertain information …
Graph neural networks: a survey on the links between privacy and security
Graph neural networks (GNNs) are models that capture the dependencies between graph
data by passing messages between graph nodes and they have been widely used to …
data by passing messages between graph nodes and they have been widely used to …
Differentially private decoupled graph convolutions for multigranular topology protection
Abstract Graph Neural Networks (GNNs) have proven to be highly effective in solving real-
world learning problems that involve graph-structured data. However, GNNs can also …
world learning problems that involve graph-structured data. However, GNNs can also …
A survey of graph neural networks in real world: Imbalance, noise, privacy and ood challenges
Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and …
domains, such as social network analysis, biochemistry, financial fraud detection, and …
Unlearning graph classifiers with limited data resources
As the demand for user privacy grows, controlled data removal (machine unlearning) is
becoming an important feature of machine learning models for data-sensitive Web …
becoming an important feature of machine learning models for data-sensitive Web …
A survey on privacy in graph neural networks: Attacks, preservation, and applications
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …
handle graph-structured data and the improvement in practical applications. However, many …
ST-ReGE: A novel spatial-temporal residual graph convolutional network for CVD
Recently, deep learning (DL) has enabled rapid advancements in electrocardiogram (ECG)-
based automatic cardiovascular disease (CVD) diagnosis. Multi-lead ECG signals have lead …
based automatic cardiovascular disease (CVD) diagnosis. Multi-lead ECG signals have lead …
Conv-RGNN: An efficient convolutional residual graph neural network for ECG classification
Background and objective: Electrocardiogram (ECG) analysis is crucial in diagnosing
cardiovascular diseases (CVDs). It is important to consider both temporal and spatial …
cardiovascular diseases (CVDs). It is important to consider both temporal and spatial …
Degree-preserving randomized response for graph neural networks under local differential privacy
S Hidano, T Murakami - arxiv preprint arxiv:2202.10209, 2022 - arxiv.org
Differentially private GNNs (Graph Neural Networks) have been recently studied to provide
high accuracy in various tasks on graph data while strongly protecting user privacy. In …
high accuracy in various tasks on graph data while strongly protecting user privacy. In …