Towards data-centric graph machine learning: Review and outlook
Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …
to drive AI models and applications, has attracted increasing attention in recent years. In this …
[BOOK][B] Deep learning on graphs
Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …
book consists of four parts to best accommodate our readers with diverse backgrounds and …
Interpretable and efficient heterogeneous graph convolutional network
Graph Convolutional Network (GCN) has achieved extraordinary success in learning
representations of nodes in graphs. However, regarding Heterogeneous Information …
representations of nodes in graphs. However, regarding Heterogeneous Information …
[BOOK][B] Introduction to graph neural networks
Graphs are useful data structures in complex real-life applications such as modeling
physical systems, learning molecular fingerprints, controlling traffic networks, and …
physical systems, learning molecular fingerprints, controlling traffic networks, and …
Graph policy network for transferable active learning on graphs
Graph neural networks (GNNs) have been attracting increasing popularity due to their
simplicity and effectiveness in a variety of fields. However, a large number of labeled data is …
simplicity and effectiveness in a variety of fields. However, a large number of labeled data is …
Collaborative knowledge distillation for heterogeneous information network embedding
Learning low-dimensional representations for Heterogeneous Information Networks (HINs)
has drawn increasing attention recently for its effectiveness in real-world applications …
has drawn increasing attention recently for its effectiveness in real-world applications …
Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA–disease association prediction
As the abnormalities of long non-coding RNAs (lncRNAs) are closely related to various
human diseases, identifying disease-related lncRNAs is important for understanding the …
human diseases, identifying disease-related lncRNAs is important for understanding the …
[HTML][HTML] A unified active learning framework for annotating graph data for regression task
In many domains, effectively applying machine learning models requires a large number of
annotations and labelled data, which might not be available in advance. Acquiring …
annotations and labelled data, which might not be available in advance. Acquiring …
Deep learning for predicting patent application outcome: The fusion of text and network embeddings
Patents have been increasingly used as an instrument to study innovation strategies and
financial performance of firms recently. Early prediction of patent application success can …
financial performance of firms recently. Early prediction of patent application success can …
A unified active learning framework for annotating graph data with application to software source code performance prediction
Most machine learning and data analytics applications, including performance engineering
in software systems, require a large number of annotations and labelled data, which might …
in software systems, require a large number of annotations and labelled data, which might …