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We study feature propagation on graph, an inference process involved in graph representation learning tasks.
M-matrix characterizations. i—nonsingular m-matrices
Robert J Plemmons · 1977
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The pagerank citation ranking: Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd · 1999
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2002
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Updating markov chains with an eye on google’s pagerank
Amy N Langville and Carl D Meyer · 2006
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Pagerank with priors: An influence propagation perspective
Biao Xiang, Qi Liu, Enhong Chen, Hui Xiong, Yi Zheng, and Yu Yang · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Grarep: Learning graph representations with global structural information
Shaosheng Cao, Wei Lu, and Qiongkai Xu · 2015
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Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Watch your step: Learning graph embeddings through attention
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, and Alex Alemi · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Poster: Neural network-based graph embedding for malicious accounts detection
Ziqi Liu, Chaochao Chen, Jun Zhou, Xiaolong Li, Feng Xu, Tao Chen, and Le Song · 2017
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Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec · 2017
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