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 …
Self-supervised heterogeneous graph neural network with co-contrastive learning
Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown
superior capacity of dealing with heterogeneous information network (HIN). However, most …
superior capacity of dealing with heterogeneous information network (HIN). However, most …
Learning intents behind interactions with knowledge graph for recommendation
Knowledge graph (KG) plays an increasingly important role in recommender systems. A
recent technical trend is to develop end-to-end models founded on graph neural networks …
recent technical trend is to develop end-to-end models founded on graph neural networks …
Heterogeneous graph contrastive learning for recommendation
Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured
data in recommender systems. However, real-life recommendation scenarios usually involve …
data in recommender systems. However, real-life recommendation scenarios usually involve …
A survey on knowledge graph-based recommender systems
To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …
applications, recommender systems have been developed to model users' preferences …
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 …
Multi-level cross-view contrastive learning for knowledge-aware recommender system
Knowledge graph (KG) plays an increasingly important role in recommender systems.
Recently, graph neural networks (GNNs) based model has gradually become the theme of …
Recently, graph neural networks (GNNs) based model has gradually become the theme of …