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 …
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 …
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 …
Graph learning: A survey
Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …
data. Graph data can be found in a broad spectrum of application domains such as social …
Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding
A large number of real-world graphs or networks are inherently heterogeneous, involving a
diversity of node types and relation types. Heterogeneous graph embedding is to embed …
diversity of node types and relation types. Heterogeneous graph embedding is to embed …
Heterogeneous graph attention network
Graph neural network, as a powerful graph representation technique based on deep
learning, has shown superior performance and attracted considerable research interest …
learning, has shown superior performance and attracted considerable research interest …
Heterogeneous graph neural network via attribute completion
Heterogeneous information networks (HINs), also called heterogeneous graphs, are
composed of multiple types of nodes and edges, and contain comprehensive information …
composed of multiple types of nodes and edges, and contain comprehensive information …
Deep learning for community detection: progress, challenges and opportunities
As communities represent similar opinions, similar functions, similar purposes, etc.,
community detection is an important and extremely useful tool in both scientific inquiry and …
community detection is an important and extremely useful tool in both scientific inquiry and …
Leveraging meta-path based context for top-n recommendation with a neural co-attention model
Heterogeneous information network (HIN) has been widely adopted in recommender
systems due to its excellence in modeling complex context information. Although existing …
systems due to its excellence in modeling complex context information. Although existing …
Heterogeneous information network embedding for recommendation
Due to the flexibility in modelling data heterogeneity, heterogeneous information network
(HIN) has been adopted to characterize complex and heterogeneous auxiliary data in …
(HIN) has been adopted to characterize complex and heterogeneous auxiliary data in …