Fetching the paper…
Reading the bibliography…
This paper studies node classification in the inductive setting, i.e., aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test graphs.
Statistical analysis of non-lattice data
Julian Besag · 1975
Earlier work this paper cites.
A stochastic parts program and noun phrase parser for unrestricted text
Kenneth Ward Church · 1988
Earlier work this paper cites.
Loopy belief propagation for approximate inference: An empirical study
Kevin P Murphy, Yair Weiss, and Michael I Jordan · 1999
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando CN Pereira · 2001
Earlier work this paper cites.
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
Yair Weiss and William T Freeman · 2001
Earlier work this paper cites.
Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Sang and Fien De Meulder · 2003
Earlier work this paper cites.
Shallow parsing with conditional random fields
Fei Sha and Fernando Pereira · 2003
Earlier work this paper cites.
Tree-reweighted belief propagation algorithms and approximate ml estimation by pseudo-moment matching
Martin J Wainwright, Tommi S Jaakkola, and Alan S Willsky · 2003
Earlier work this paper cites.
Multiscale conditional random fields for image labeling
Xuming He, Richard S Zemel, and Miguel Á Carreira-Perpiñán · 2004
Earlier work this paper cites.
Large margin methods for structured and interdependent output variables
Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, and Yasemin Altun · 2005
Earlier work this paper cites.
Constructing free-energy approximations and generalized belief propagation algorithms
Jonathan S Yedidia, William T Freeman, and Yair Weiss · 2005
Earlier work this paper cites.
An introduction to conditional random fields for relational learning
Charles Sutton and Andrew McCallum · 2006
Earlier work this paper cites.
Predicting structured data
Gökhan BakIr, Thomas Hofmann, Bernhard Schölkopf, Alexander J Smola, and Ben Taskar · 2007
Earlier work this paper cites.
Training structural svms when exact inference is intractable
Thomas Finley and Thorsten Joachims · 2008
Earlier work this paper cites.
Accurate max-margin training for structured output spaces
Sunita Sarawagi and Rahul Gupta · 2008
Earlier work this paper cites.
Arnetminer: extraction and mining of academic social networks
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su · 2008
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael Irwin Jordan · 2008
Earlier work this paper cites.
Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman · 2009
Cited alongside, same era.
Piecewise training for structured prediction
Charles Sutton and Andrew McCallum · 2009
Cited alongside, same era.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Cited alongside, same era.
An Introduction to Conditional Random Fields
Charles Sutton and Andrew McCallum · 2012
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Empower sequence labeling with task-aware neural language model
Liyuan Liu, Jingbo Shang, Xiang Ren, Frank Xu, Huan Gui, Jian Peng, and Jiawei Han · 2018
Later among the works it cites.
Cgnf: Conditional graph neural fields
Tengfei Ma, Cao Xiao, Junyuan Shang, and Jimeng Sun · 2018
Later among the works it cites.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Later among the works it cites.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
Later among the works it cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Later among the works it cites.
Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Cited alongside, same era.
Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2016
Cited alongside, same era.
Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
Cited alongside, same era.
Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer · 2016
Cited alongside, same era.
End-to-end sequence labeling via bi-directional lstm-cnns-crf
Xuezhe Ma and Eduard Hovy · 2016
Cited alongside, same era.
Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
Cited alongside, same era.
Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
Later among the works it cites.
A flexible generative framework for graph-based semi-supervised learning
Jiaqi Ma, Weijing Tang, Ji Zhu, and Qiaozhu Mei · 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.
Combining generative and discriminative models for hybrid inference
Victor Garcia Satorras, Zeynep Akata, and Max Welling · 2019
Later among the works it cites.
Neural enhanced belief propagation on factor graphs
Victor Garcia Satorras and Max Welling · 2020
Later among the works it cites.
Continuous graph neural networks
Louis-Pascal Xhonneux, Meng Qu, and Jian Tang · 2020
Later among the works it cites.
Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2020
Later among the works it cites.
Efficient probabilistic logic reasoning with graph neural networks
Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, and Le Song · 2020
Later among the works it cites.
Copulagnn: Towards integrating representational and correlational roles of graphs in graph neural networks
Jiaqi Ma, Bo Chang, Xuefei Zhang, and Qiaozhu Mei · 2021
Later among the works it cites.
Semi-supervised node classification on graphs: Markov random fields vs. graph neural networks
Binghui Wang, Jinyuan Jia, and Neil Zhenqiang Gong · 2021
Later among the works it cites.