A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
Utilizing graph machine learning within drug discovery and development
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …
biotechnology industries for its ability to model biomolecular structures, the functional …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Heterogeneous graph neural network
Representation learning in heterogeneous graphs aims to pursue a meaningful vector
representation for each node so as to facilitate downstream applications such as link …
representation for each node so as to facilitate downstream applications such as link …
Deep learning on graphs: A survey
Deep learning has been shown to be successful in a number of domains, ranging from
acoustics, images, to natural language processing. However, applying deep learning to the …
acoustics, images, to natural language processing. However, applying deep learning to the …
Graph representation learning in bioinformatics: trends, methods and applications
HC Yi, ZH You, DS Huang… - Briefings in …, 2022 - academic.oup.com
Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …
[图书][B] Neural networks and deep learning
CC Aggarwal - 2018 - Springer
“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …
McDonald Neural networks were developed to simulate the human nervous system for …
A semi-supervised graph attentive network for financial fraud detection
With the rapid growth of financial services, fraud detection has been a very important
problem to guarantee a healthy environment for both users and providers. Conventional …
problem to guarantee a healthy environment for both users and providers. Conventional …
A comprehensive survey of graph embedding: Problems, techniques, and applications
Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …
scenarios. Effective graph analytics provides users a deeper understanding of what is …