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 …
Are meta-paths necessary? Revisiting heterogeneous graph embeddings
The graph embedding paradigm projects nodes of a graph into a vector space, which can
facilitate various downstream graph analysis tasks such as node classification and …
facilitate various downstream graph analysis tasks such as node classification and …
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 …
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 …
Understanding graph embedding methods and their applications
M Xu - SIAM Review, 2021 - SIAM
Graph analytics can lead to better quantitative understanding and control of complex
networks, but traditional methods suffer from the high computational cost and excessive …
networks, but traditional methods suffer from the high computational cost and excessive …
Dynamic heterogeneous graph embedding using hierarchical attentions
Graph embedding has attracted many research interests. Existing works mainly focus on
static homogeneous/heterogeneous networks or dynamic homogeneous networks …
static homogeneous/heterogeneous networks or dynamic homogeneous networks …
Graphzoom: A multi-level spectral approach for accurate and scalable graph embedding
Graph embedding techniques have been increasingly deployed in a multitude of different
applications that involve learning on non-Euclidean data. However, existing graph …
applications that involve learning on non-Euclidean data. However, existing graph …
Graph embedding techniques, applications, and performance: A survey
Graphs, such as social networks, word co-occurrence networks, and communication
networks, occur naturally in various real-world applications. Analyzing them yields insight …
networks, occur naturally in various real-world applications. Analyzing them yields insight …
Heterogeneous graph representation learning with relation awareness
Representation learning on heterogeneous graphs aims to obtain meaningful node
representations to facilitate various downstream tasks, such as node classification and link …
representations to facilitate various downstream tasks, such as node classification and link …
Multiplex heterogeneous graph convolutional network
Heterogeneous graph convolutional networks have gained great popularity in tackling
various network analytical tasks on heterogeneous network data, ranging from link …
various network analytical tasks on heterogeneous network data, ranging from link …