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 …
Knowledge graphs
In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …
recently garnered significant attention from both industry and academia in scenarios that …
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 …
Financial time series forecasting with multi-modality graph neural network
Financial time series analysis plays a central role in hedging market risks and optimizing
investment decisions. This is a challenging task as the problems are always accompanied …
investment decisions. This is a challenging task as the problems are always accompanied …
Heterogeneous network representation learning: A unified framework with survey and benchmark
Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …
heterogeneous networks have been widely used as a more powerful, realistic, and generic …
Revisiting user mobility and social relationships in lbsns: a hypergraph embedding approach
Location Based Social Networks (LBSNs) have been widely used as a primary data source
to study the impact of mobility and social relationships on each other. Traditional …
to study the impact of mobility and social relationships on each other. Traditional …
Sampling methods for efficient training of graph convolutional networks: A survey
Graph convolutional networks (GCNs) have received significant attention from various
research fields due to the excellent performance in learning graph representations. Although …
research fields due to the excellent performance in learning graph representations. Although …
Beyond triplets: hyper-relational knowledge graph embedding for link prediction
Knowledge Graph (KG) embeddings are a powerful tool for predicting missing links in KGs.
Existing techniques typically represent a KG as a set of triplets, where each triplet (h, r, t) …
Existing techniques typically represent a KG as a set of triplets, where each triplet (h, r, t) …
Dynamic heterogeneous information network embedding with meta-path based proximity
Heterogeneous information network (HIN) embedding aims at learning the low-dimensional
representation of nodes while preserving structure and semantics in a HIN. Existing methods …
representation of nodes while preserving structure and semantics in a HIN. Existing methods …