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A survey on hypergraph representation learning
Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …
naturally modeling a broad range of systems where high-order relationships exist among …
Graph representation learning in biomedicine and healthcare
Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …
biomedicine and healthcare, they can represent, for example, molecular interactions …
Simple and efficient heterogeneous graph neural network
Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich
structural and semantic information of a heterogeneous graph into node representations …
structural and semantic information of a heterogeneous graph into node representations …
Gpt-gnn: Generative pre-training of graph neural networks
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-
structured data. However, training GNNs requires abundant task-specific labeled data …
structured data. However, training GNNs requires abundant task-specific labeled data …
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 …
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 …
A survey on graph neural network acceleration: Algorithms, systems, and customized hardware
Graph neural networks (GNNs) are emerging for machine learning research on graph-
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …
Node classification oriented adaptive multichannel heterogeneous graph neural network
Heterogeneous graph neural networks (HGNNs) play an important role in accomplishing
node classification on heterogeneous graphs (HGs). These models are built on the …
node classification on heterogeneous graphs (HGs). These models are built on the …
Heterformer: Transformer-based deep node representation learning on heterogeneous text-rich networks
Representation learning on networks aims to derive a meaningful vector representation for
each node, thereby facilitating downstream tasks such as link prediction, node classification …
each node, thereby facilitating downstream tasks such as link prediction, node classification …
Collaborative knowledge distillation for heterogeneous information network embedding
Learning low-dimensional representations for Heterogeneous Information Networks (HINs)
has drawn increasing attention recently for its effectiveness in real-world applications …
has drawn increasing attention recently for its effectiveness in real-world applications …