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Deep learning models have achieved huge success in numerous fields, such as computer vision and natural language processing.
gspan: Graph-based substructure pattern mining
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Discriminative feature selection for uncertain graph classification
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Grami: Frequent subgraph and pattern mining in a single large graph
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Large-scale frequent subgraph mining in mapreduce
Wenqing Lin, Xiaokui Xiao, and Gabriel Ghinita · 2014
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Boosting for multi-graph classification
Jia Wu, Shirui Pan, Xingquan Zhu, and Zhihua Cai · 2014
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Learning entity and relation embeddings for knowledge graph completion
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Geodesic convolutional neural networks on riemannian manifolds
Jonathan Masci, Davide Boscaini, Michael Bronstein, and Pierre Vandergheynst · 2015
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Nonlinear graph fusion for multi-modal classification of alzheimer’s disease
Tong Tong, Katherine Gray, Qinquan Gao, Liang Chen, and Daniel Rueckert · 2015
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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
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Geometric deep learning on graphs and manifolds using mixture model cnns
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graph2vec: Learning distributed representations of graphs
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Multi-modal classification of alzheimer’s disease using nonlinear graph fusion
Tong Tong, Katherine Gray, Qinquan Gao, Liang Chen, Daniel Rueckert, Alzheimer’s Disease Neuroimaging Initiative, et al · 2017
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Structural deep brain network mining
Shen Wang, Lifang He, Bokai Cao, Chun-Ta Lu, Philip S Yu, and Ann B Ragin · 2017
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Watch your step: Learning node embeddings via graph attention
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, and Alexander A Alemi · 2018
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
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Relational inductive biases, deep learning, and graph networks
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Graph classification using structural attention
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A parallel approach for frequent subgraph mining in a single large graph using spark
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