Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions
Deep learning has seen significant growth recently and is now applied to a wide range of
conventional use cases, including graphs. Graph data provides relational information …
conventional use cases, including graphs. Graph data provides relational information …
A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability
Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …
their great ability in modeling graph-structured data, GNNs are vastly used in various …
Building a knowledge graph to enable precision medicine
Develo** personalized diagnostic strategies and targeted treatments requires a deep
understanding of disease biology and the ability to dissect the relationship between …
understanding of disease biology and the ability to dissect the relationship between …
Graph prompt learning: A comprehensive survey and beyond
Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration
with graph data, a cornerstone in our interconnected world, remains nascent. This paper …
with graph data, a cornerstone in our interconnected world, remains nascent. This paper …
[PDF][PDF] Deep learning approaches for medical image analysis and diagnosis
In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential to
streamline workflows, reduce interpretation time, and ultimately improve patient outcomes …
streamline workflows, reduce interpretation time, and ultimately improve patient outcomes …
Prog: A graph prompt learning benchmark
Artificial general intelligence on graphs has shown significant advancements across various
applications, yet the traditional'Pre-train & Fine-tune'paradigm faces inefficiencies and …
applications, yet the traditional'Pre-train & Fine-tune'paradigm faces inefficiencies and …
Predicting microbe–drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy
Motivation Predicting the associations between human microbes and drugs (MDAs) is one
critical step in drug development and precision medicine areas. Since discovering these …
critical step in drug development and precision medicine areas. Since discovering these …
Using graph neural network to conduct supplier recommendation based on large-scale supply chain
Driven by economic globalisation, various industries have developed a trend towards high
specialisation and vertical division of labor, resulting in vast and intricate supply chain …
specialisation and vertical division of labor, resulting in vast and intricate supply chain …
Subgraph-aware graph kernel neural network for link prediction in biological networks
Identifying links within biological networks is important in various biomedical applications.
Recent studies have revealed that each node in a network may play a unique role in …
Recent studies have revealed that each node in a network may play a unique role in …
Community preserving adaptive graph convolutional networks for link prediction in attributed networks
Link prediction in attributed networks has attracted increasing attention recently due to its
valuable real-world applications. Various related methods have been proposed, but most of …
valuable real-world applications. Various related methods have been proposed, but most of …