Who Cited It

Heterogeneous Graph Attention Network

2019 · 2,999 citations · 7 from inside this corpus

Xiao Wang, Houye Ji, Chuan Shi, Bai Wang low, Yanfang Ye, Peng Cui, Philip S. Yu

Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its meta-path based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.

Heterogeneous Graph Attention Network (2019)Heterogeneous Graph Attention…Exploiting Generative AI to Scale up Intelligent Tutoring Systems (2023)Exploiting Generative AI to S…[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…The Graph Neural Network Model (2008)The Graph Neural Network ModelDeepWalk (2014)DeepWalkSemi-Supervised Classification with Graph Convolutional Networks (2016)Semi-Supervised Classificatio…Modeling Relational Data with Graph Convolutional Networks (2018)Modeling Relational Data with…Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Netsci-X'… (2016)Convolutional Neural Networks…LINE (2015)LINEStructural Deep Network Embedding (2016)Structural Deep Network Embed…metapath2vec (2017)metapath2vecA new model for learning in graph domains (2006)A new model for learning in g…PathSim (2011)PathSimConvolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (2016)Convolutional Neural Networks…A Comprehensive Survey on Graph Neural Networks (2020)A Comprehensive Survey on Gra…Graph neural networks: A review of methods and applications (2020)Graph neural networks: A revi…A Comprehensive Survey on Graph Neural Networks (2020)A Comprehensive Survey on Gra…Knowledge Graphs (2021)Knowledge GraphsDeep Learning on Graphs: A Survey (2020)Deep Learning on Graphs: A Su…Graph Neural Networks: A Review of Methods and Applications (2018)Graph Neural Networks: A Revi…Heterogeneous Graph Transformer (2020)Heterogeneous Graph Transform…
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What this paper cites, inside the corpus

PaperYearCited
Exploiting Generative AI to Scale up Intelligent Tutoring Systems202379,071
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
The Graph Neural Network Model20089,662
DeepWalk20148,670
Semi-Supervised Classification with Graph Convolutional Networks20168,058
Modeling Relational Data with Graph Convolutional Networks20185,233
Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Netsci-X'…20165,118
LINE20154,752
Structural Deep Network Embedding20162,843
metapath2vec20172,291
A new model for learning in graph domains20061,944
PathSim20111,928
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering20161,703

What cites it, inside the corpus

PaperYearCited
A Comprehensive Survey on Graph Neural Networks20209,855
Graph neural networks: A review of methods and applications20205,808
A Comprehensive Survey on Graph Neural Networks20203,307
Knowledge Graphs20211,767
Deep Learning on Graphs: A Survey20201,560
Graph Neural Networks: A Review of Methods and Applications20181,448
Heterogeneous Graph Transformer20201,432

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Topics

Advanced Graph Neural NetworksComputer Science
Complex Network Analysis TechniquesPhysics and Astronomy
Topic ModelingComputer Science

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complete

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  • supports7 author record(s) attached.
  • supports45 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

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