Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
Learning probabilistic models of relational structure
Lise Getoor, Nir Friedman, Daphne Koller, and Benjamin Taskar · 2001
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.
Cluster kernels for semi-supervised learning
Olivier Chapelle, Jason Weston, and Bernhard Schölkopf · 2003
Earlier work this paper cites.
Transductive learning via spectral graph partitioning
Thorsten Joachims · 2003
Earlier work this paper cites.
Mixing patterns in networks
Mark EJ Newman · 2003
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and J. Lafferty · 2003
Earlier work this paper cites.
Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf · 2004
Earlier work this paper cites.
Link-based classification
Lise Getoor · 2005
Earlier work this paper cites.
Graph evolution: Densification and shrinking diameters
Jure Leskovec, Jon Kleinberg, and Christos Faloutsos · 2007
Earlier work this paper cites.
Near linear time algorithm to detect community structures in large-scale networks
Usha Nandini Raghavan, Réka Albert, and Soundar Kumara · 2007
Earlier work this paper cites.
Label propagation through linear neighborhoods
Fei Wang and Changshui Zhang · 2007
Earlier work this paper cites.
Networks, crowds, and markets
David Easley and Jon Kleinberg · 2010
Earlier work this paper cites.
It’s who you know: graph mining using recursive structural features
Keith Henderson, Brian Gallagher, Lei Li, Leman Akoglu, Tina Eliassi-Rad, Hanghang Tong, and Christos Faloutsos · 2011
Earlier work this paper cites.
Unifying guilt-by-association approaches: Theorems and fast algorithms
Danai Koutra, Tai-You Ke, U Kang, Duen Horng Polo Chau, Hsing-Kuo Kenneth Pao, and Christos Faloutsos · 2011
Earlier work this paper cites.
Spectral clustering of graphs with general degrees in the extended planted partition model
Kamalika Chaudhuri, Fan Chung, and Alexander Tsiatas · 2012
Earlier work this paper cites.
RolX: structural role extraction & mining in large graphs
Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Christos Faloutsos, and Lei Li · 2012
Earlier work this paper cites.
Query-driven active surveying for collective classification
Galileo Namata, Ben London, Lise Getoor, and Bert Huang · 2012
Earlier work this paper cites.
Social structure of facebook networks
Amanda L Traud, Peter J Mucha, and Mason A Porter · 2012
Earlier work this paper cites.
Advanced data analysis from an elementary point of view
Cosma Shalizi · 2013
Cited alongside, same era.
Using local spectral methods to robustify graph-based learning algorithms
David F Gleich and Michael W Mahoney · 2015
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Aditya Grover and J. Leskovec · 2016
Cited alongside, same era.
Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Graph-based semi-supervised learning for relational networks
Leto Peel · 2017
Cited alongside, same era.
Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Müller, Ali Thabet, and Bernard Ghanem · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
GMNN: Graph markov neural networks
Meng Qu, Yoshua Bengio, and Jian Tang · 2019
Later among the works it cites.
Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2019
Later among the works it cites.
Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
Later among the works it cites.
GraphSAINT: Graph sampling based inductive learning method
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Local higher-order graph clustering
Hao Yin, Austin R Benson, Jure Leskovec, and David F Gleich · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
P. Battaglia, Jessica B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, Mateusz Malinowski, Andrea Tacchetti, D. Raposo, Adam Santoro, R. Faulkner, Çaglar Gülçehre, H. Song, A. Ballard, J. Gilmer, G. Dahl, Ashish Vaswani, Kelsey R. Allen, C. Nash, V. Langston, Chris Dyer, N. Heess, Daan Wierstra, Pushmeet Kohli, M. Botvinick, Oriol Vinyals, Y. Li, and Razvan Pascanu · 2018
Cited alongside, same era.
Bootstrapped graph diffusions: Exposing the power of nonlinearity
Buchnik Eliav and Edith Cohen · 2018
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Cited alongside, same era.
UMAP: Uniform Manifold Approximation and Projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger · 2018
Cited alongside, same era.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2019
Later among the works it cites.
Scaling graph neural networks with approximate pagerank
Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann · 2020
Closest in time.
Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2020
Closest in time.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, M. Fey, M. Zitnik, Yuxiao Dong, H. Ren, Bowen Liu, Michele Catasta, and J. Leskovec · 2020
Closest in time.
Residual correlation in graph neural network regression
Junteng Jia and Austin R. Benson · 2020
Closest in time.
Wiki-cs: A wikipedia-based benchmark for graph neural networks
P’eter Mernyei and Cătălina Cangea · 2020
Closest in time.
SIGN: Scalable inception graph neural networks
Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael Bronstein, and Federico Monti · 2020
Closest in time.
Masked label prediction: Unified massage passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, and Yu Sun · 2020
Closest in time.
Nonlinear higher-order label spreading
Francesco Tudisco, Austin R Benson, and Konstantin Prokopchik · 2020
Closest in time.
Unifying graph convolutional neural networks and label propagation
Hongwei Wang and Jure Leskovec · 2020
Closest in time.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, C. Zhang, and Philip S. Yu · 2020
Closest in time.
Semi-supervised learning literature survey
Xiaojin Jerry Zhu · 2020
Closest in time.