A comprehensive survey of graph neural networks for knowledge graphs
The Knowledge graph, a multi-relational graph that represents rich factual information
among entities of diverse classifications, has gradually become one of the critical tools for …
among entities of diverse classifications, has gradually become one of the critical tools for …
Hyperbolic graph neural networks: A review of methods and applications
Graph neural networks generalize conventional neural networks to graph-structured data
and have received widespread attention due to their impressive representation ability. In …
and have received widespread attention due to their impressive representation ability. In …
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 …
Geometry interaction knowledge graph embeddings
Abstract Knowledge graph (KG) embeddings have shown great power in learning
representations of entities and relations for link prediction tasks. Previous work usually …
representations of entities and relations for link prediction tasks. Previous work usually …
Pasca: A graph neural architecture search system under the scalable paradigm
Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …
based tasks. However, as mainstream GNNs are designed based on the neural message …
Self-supervised continual graph learning in adaptive riemannian spaces
Continual graph learning routinely finds its role in a variety of real-world applications where
the graph data with different tasks come sequentially. Despite the success of prior works, it …
the graph data with different tasks come sequentially. Despite the success of prior works, it …
Learning knowledge graph embedding with multi-granularity relational augmentation network
Abstract Knowledge graph embedding (KGE) aims to complete link prediction tasks
effectively by learning the representation of entity and relation. Recently, deep neural …
effectively by learning the representation of entity and relation. Recently, deep neural …
Motif-aware riemannian graph neural network with generative-contrastive learning
Graphs are typical non-Euclidean data of complex structures. Recently, Riemannian graph
representation learning emerges as an exciting alternative to the traditional Euclidean ones …
representation learning emerges as an exciting alternative to the traditional Euclidean ones …
UUKG: unified urban knowledge graph dataset for urban spatiotemporal prediction
Abstract Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the
development and operation of the smart city. As an emerging building block, multi-sourced …
development and operation of the smart city. As an emerging building block, multi-sourced …
Graph neural pre-training for recommendation with side information
Leveraging the side information associated with entities (ie, users and items) to enhance
recommendation systems has been widely recognized as an essential modeling dimension …
recommendation systems has been widely recognized as an essential modeling dimension …