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The area of graph embeddings is currently dominated by contrastive learning methods, which demand formulation of an explicit objective function and sampling of positive and negative examples.
Principal Component Analysis and Factor Analysis
I. T. Jolliffe · 1986
Earlier work this paper cites.
Mining associations between sets of items in large databases
Rakesh Agrawal, Tomasz Imielinski, and Arun Swami · 1993
Earlier work this paper cites.
Fast algorithms for mining association rules
Rakesh Agarwal, Ramakrishnan Srikant, et al · 1994
Earlier work this paper cites.
Multidimensional Scaling, Second Edition
Trevor F. Cox and M.A.A. Cox · 2000
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
Joshua B. Tenenbaum, Vin de Silva, and John C. Langford · 2000
Earlier work this paper cites.
Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook · 2001
Earlier work this paper cites.
Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2002
Earlier work this paper cites.
Efficiently using prefix-trees in mining frequent itemsets
Gösta Grahne and Jianfei Zhu · 2003
Earlier work this paper cites.
Group formation in large social networks: Membership, growth, and evolution
Lars Backstrom, Dan Huttenlocher, Jon Kleinberg, and Xiangyang Lan · 2006
Earlier work this paper cites.
The link-prediction problem for social networks
David Liben-Nowell and Jon Kleinberg · 2007
Earlier work this paper cites.
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Earlier work this paper cites.
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Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, and Bobby Bhattacharjee · 2007
Earlier work this paper cites.
Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters
Jure Leskovec, Kevin Lang, Anirban Dasgupta, and Michael Mahoney · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
What is Twitter, a social network or a news media?
Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon · 2010
Earlier work this paper cites.
A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel · 2011
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
Earlier work this paper cites.
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SNAP Datasets: Stanford large network dataset collection
Jure Leskovec and Andrej Krevl · 2014
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Grarep: Learning graph representations with global structural information
Shaosheng Cao, Wei Lu, and Qiongkai Xu · 2015
Cited alongside, same era.
Inferring networks of substitutable and complementary products
Harp: Hierarchical representation learning for networks
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Verse: Versatile graph embeddings from similarity measures
Anton Tsitsulin, Davide Mottin, Panagiotis Karras, and Emmanuel Müller · 2018
Later among the works it cites.
Cosine: Community-preserving social network embedding from information diffusion cascades
Yuan Zhang, Tianshu Lyu, and Yan Zhang · 2018
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Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
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PyTorch-BigGraph: A Large-scale Graph Embedding System
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