Fetching the paper…
Reading the bibliography…
Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features.
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2019a · 1901
Earlier work this paper cites.
Near-linear Time Approximation Algorithms for Optimal Transport via Sinkhorn Iteration. In Advances in Neural Information Processing Systems 30 . 1964–1974
Jason Altschuler and Jonathan Weed. 2017 · 1974
Earlier work this paper cites.
Collective Classification in Network Data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad. 2008 · 2008
Earlier work this paper cites.
Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey E. Hinton. 2008 · 2008
Earlier work this paper cites.
Sinkhorn Distances: Lightspeed Computation of Optimal Transport. In Advances in Neural Information Processing Systems 26 . 2292–2300
Marco Cuturi. 2013 · 2013
Earlier work this paper cites.
Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems 26 . 3111–3119
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
DeepWalk: Online Learning of Social Representations. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 701–710
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
Earlier work this paper cites.
GraRep: Learning Graph Representations with Global Structural Information. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management . ACM, 891–900
Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015 · 2015
Earlier work this paper cites.
LINE: Large-scale Information Network Embedding. In Proceedings of the 24th International Conference on World Wide Web . ACM, 1067–1077
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015 · 2015
Earlier work this paper cites.
Deep Neural Networks for Learning Graph Representations. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence . AAAI, 1145–1152
Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016 · 2016
Earlier work this paper cites.
node2vec: Scalable Feature Learning for Networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 855–864
Aditya Grover and Jure Leskovec. 2016 · 2016
Earlier work this paper cites.
Variational Graph Auto-Encoders
Thomas N. Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles. In Proceedings of the 14th European Conference on Computer Vision . 69–84
Mehdi Noroozi and Paolo Favaro. 2016 · 2016
Cited alongside, same era.
Context Encoders: Feature Learning by Inpainting. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2536–2544
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. 2016 · 2016
Cited alongside, same era.
Revisiting Semi-Supervised Learning with Graph Embeddings. In Proceedings of the 33rd International Conference on Machine Learning , Vol. 48. PMLR, 40–48
Zhilin Yang, William W. Cohen, and Ruslan R. Salakhutdinov. 2016 · 2016
Cited alongside, same era.
Colorful Image Colorization. In Proceedings of the 14th European Conference on Computer Vision . 649–666
Richard Zhang, Phillip Isola, and Alexei A Efros. 2016 · 2016
Cited alongside, same era.
Representation Learning on Graphs: Methods and Applications
Unsupervised Representation Learning by Predicting Image Rotations. In Proceedings of the 6th International Conference on Learning Representations . OpenReview.net
Spyros Gidaris, Praveer Singh, and Nikos Komodakis. 2018 · 2018
Later among the works it cites.
Boosting Self-Supervised Learning via Knowledge Transfer. In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 9359–9367
Mehdi Noroozi, Ananth Vinjimoor, Paolo Favaro, and Hamed Pirsiavash. 2018 · 2018
Later among the works it cites.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . ACM, 459–467
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018 · 2018
Later among the works it cites.
Graph Attention Networks. In Proceedings of the 6th International Conference on Learning Representations . OpenReview.net
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017a · 2017
Cited alongside, same era.
Accelerated Attributed Network Embedding. In Proceedings of the 2017 SIAM International Conference on Data Mining . SIAM, 633–641
Xiao Huang, Jundong Li, and Xia Hu. 2017 · 2017
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations . OpenReview.net
Thomas N. Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Colorization as a Proxy Task for Visual Understanding. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 840–849
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich. 2017 · 2017
Cited alongside, same era.
struc2vec: Learning Node Representations from Structural Identity. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 385–394
Leonardo Filipe Rodrigues Ribeiro, Pedro H. P. Saverese, and Daniel R. Figueiredo. 2017 · 2017
Cited alongside, same era.
MGAE: Marginalized Graph Autoencoder for Graph Clustering. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . ACM, 889–898
Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang. 2017b · 2017
Cited alongside, same era.
Deep Clustering for Unsupervised Learning of Visual Features. In Proceedings of the 15th European Conference on Computer Vision . 139–156
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. 2018 · 2018
Cited alongside, same era.
Deep Attributed Network Embedding. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence . IJCAI, 3364–3370
Hongchang Gao and Heng Huang. 2018 · 2018
Cited alongside, same era.
Link Prediction Based on Graph Neural Networks. In Advances in Neural Information Processing Systems 31 . 5167–5177
Muhan Zhang and Yixin Chen. 2018 · 2018
Later among the works it cites.
Deep Graph Infomax. In Proceedings of the 7th International Conference on Learning Representations . OpenReview.net
Petar Veličković, William Fedus, William L. Hamilton, and Pietro Liò. 2019 · 2019
Later among the works it cites.
Attributed Graph Clustering: A Deep Attentional Embedding Approach. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence . IJCAI, 3670–3676
Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019 · 2019
Later among the works it cites.
How Powerful are Graph Neural Networks?. In Proceedings of the 7th International Conference on Learning Representations
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Later among the works it cites.
Attributed Graph Clustering via Adaptive Graph Convolution. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence . 4327–4333
Xiaotong Zhang, Han Liu, Qimai Li, and Xiao-Ming Wu. 2019 · 2019
Later among the works it cites.
Self-labelling via Simultaneous Clustering and Representation Learning. In Proceedings of the 8th International Conference on Learning Representations . OpenReview.net
Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi. 2020 · 2020
Closest in time.
GPT-GNN: Generative Pre-Training of Graph Neural Networks. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, and Yizhou Sun. 2020 · 2020
Closest in time.
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence . AAAI, 5892–5899
Ke Sun, Zhouchen Lin, and Zhanxing Zhu. 2020 · 2020
Closest in time.