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Graph Neural Networks (GNN) have been extensively used to extract meaningful representations from graph structured data and to perform predictive tasks such as node classification and link prediction.
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 1901
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A Comparative Study for Unsupervised Network Representation Learning
Megha Khosla, Vinay Setty, and Avishek Anand · 1903
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Sparse Graph Attention Networks
Yang Ye and Shihao Ji · 1912
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A Global Geometric Framework for Nonlinear Dimensionality Reduction
J. B. Tenenbaum · 2000
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Bochner’s method for cell complexes and combinatorial ricci curvature
Robin Forman · 2003
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Fast unfolding of communities in large networks
Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre · 2008
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Understanding the difficulty of training deep feedforward neural networks, 2010
Xavier Glorot and Yoshua Bengio · 2010
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Efficient computation of the shapley value for game-theoretic network centrality
T. P. Michalak, K. V. Aadithya, P. L. Szczepanski, B. Ravindran, and N. R. Jennings · 2013
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Adam: A method for stochastic optimization, 2014
Diederik P. Kingma and Jimmy Ba · 2014
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Order matters: Sequence to sequence for sets, 2015
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 2015
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Spatial reconstruction of single-cell gene expression data
Rahul Satija, Jeffrey A Farrell, David Gennert, Alexander F Schier, and Aviv Regev · 2015
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Low data drug discovery with one-shot learning, 2016
Han Altae-Tran, Bharath Ramsundar, Aneesh S. Pappu, and Vijay Pande · 2016
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node2vec: Scalable feature learning for networks, 2016
Aditya Grover and Jure Leskovec · 2016
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Layer normalization, 2016
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Fast and accurate deep network learning by exponential linear units (elus), 2016
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2016
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Revisiting semi-supervised learning with graph embeddings, 2016
Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov · 2016
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Semi-supervised classification with graph convolutional networks, 2017
Thomas N. Kipf and Max Welling · 2017
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Massively parallel digital transcriptional profiling of single cells
Grace X. Y. Zheng, Jessica M. Terry, Phillip Belgrader, Paul Ryvkin, Zachary W. Bent, Ryan Wilson, Solongo B. Ziraldo, Tobias D. Wheeler, Geoff P. McDermott, Junjie Zhu, Mark T. Gregory, Joe Shuga, Luz Montesclaros, Jason G. Underwood, Donald A. Masquelier, Stefanie Y. Nishimura, Michael Schnall-Levin, Paul W. Wyatt, Christopher M. Hindson, Rajiv Bharadwaj, Alexander Wong, Kevin D. Ness, Lan W. Beppu, H. Joachim Deeg, Christopher McFarland, Keith R. Loeb, William J. Valente, Nolan G. Ericson, Emily A. Stevens, Jerald P. Radich, Tarjei S. Mikkelsen, Benjamin J. Hindson, and Jason H. Bielas · 2017
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Learning edge representations via low-rank asymmetric projections
Sami Abu-El-Haija, Bryan Perozzi, and Rami Al-Rfou · 2017
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A unified approach to interpreting model predictions, 2017
Scott Lundberg and Su-In Lee · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Gram: Graph-based attention model for healthcare representation learning
Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, and Jimeng Sun · 2017
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Combinatorial ricci curvature on cell-complex and gauss-bonnnet theorem, 2017
Kazuyoshi Watanabe · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
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Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Exploiting edge features in graph neural networks, 2018
Liyu Gong and Qiang Cheng · 2018
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edge2vec: Representation learning using edge semantics for biomedical knowledge discovery, 2018
Zheng Gao, Gang Fu, Chunping Ouyang, Satoshi Tsutsui, Xiaozhong Liu, Jeremy Yang, Christopher Gessner, Brian Foote, David Wild, Qi Yu, and Ying Ding · 2018
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Single-cell RNA sequencing technologies and bioinformatics pipelines
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks, 2019
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
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Attention models in graphs: A survey
John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, and Eunyee Koh · 2019
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How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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A test metric for assessing single-cell RNA-seq batch correction
Maren Büttner, Zhichao Miao, F. Alexander Wolf, Sarah A. Teichmann, and Fabian J. Theis · 2019
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Digitaldlsorter: Deep-learning on scrna-seq to deconvolute gene expression data
Carlos Torroja and Fatima Sanchez-Cabo · 2019
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Byungjin Hwang, Ji Hyun Lee, and Duhee Bang · 2018
Cited alongside, same era.
Set transformer: A framework for attention-based permutation-invariant neural networks, 2018
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, and Yee Whye Teh · 2018
Cited alongside, same era.
Graph attention networks, 2018
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Comparative analysis of two discretizations of ricci curvature for complex networks
Areejit Samal, R. P. Sreejith, Jiao Gu, Shiping Liu, Emil Saucan, and Jürgen Jost · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection for dimension reduction, 2018
Leland McInnes, John Healy, and James Melville · 2018
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Link prediction based on graph neural networks, 2018
Muhan Zhang and Yixin Chen · 2018
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Scanpy: large-scale single-cell gene expression data analysis
F. Alexander Wolf, Philipp Angerer, and Theis Fabian J · 2018
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Deepimpute: an accurate, fast, and scalable deep neural network method to impute single-cell rna-seq data
Cédric Arisdakessian, Olivier Poirion, Breck Yunits, Xun Zhu, and Lana X Garmire · 2019
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Exploring single-cell data with deep multitasking neural networks
Matthew Amodio, David van Dijk, Krishnan Srinivasan, William S Chen, Hussein Mohsen, Kevin R Moon, Allison Campbell, Yujiao Zhao, Xiaomei Wang, Manjunatha Venkataswamy, Anita Desai, V Ravi, Priti Kumar, Ruth Montgomery, Guy Wolf, and Smita Krishnaswamy · 2019
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Data denoising with transfer learning in single-cell transcriptomics
Jingshu Wang, Divyansh Agarwal, Mo Huang, Gang Hu, Zilu Zhou, Chengzhong Ye, and Nancy R. Zhang · 2019
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Graph embedding on biomedical networks: methods, applications and evaluations
Xiang Yue, Zhen Wang, Jingong Huang, Srinivasan Parthasarathy, Soheil Moosavinasab, Yungui Huang, Simon M Lin, Wen Zhang, Ping Zhang, and Huan Sun · 2019
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A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases
Danuta R Gawel, Jordi Serra-Musach, Sandra Lilja, Jesper Aagesen, Alex Arenas, Bengt Asking, Malin Bengnér, Janne Björkander, Sophie Biggs, Jan Ernerudh, Henrik Hjortswang, Jan-Erik Karlsson, Mattias Köpsen, Eun Jung Lee, Antonio Lentini, Xinxiu Li, Mattias Magnusson, David Martínez-Enguita, Andreas Matussek, Colm E Nestor, Samuel Schäfer, Oliver Seifert, Ceylan Sonmez, Henrik Stjernman, Andreas Tjärnberg, Simon Wu, Karin Åkesson, Alex K Shalek, Margaretha Stenmarker, Huan Zhang, Mika Gustafsson, and Mikael Benson · 2019
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Single-cell rna sequencing-based computational analysis to describe disease heterogeneity
Tao Zeng and Hao Dai · 2019
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scGen predicts single-cell perturbation responses
Mohammad Lotfollahi, F Alexander Wolf, and Fabian J Theis · 2019
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Emerging deep learning methods for single-cell RNA-seq data analysis
Jie Zheng and Ke Wang · 2019
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
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Pytorch captum
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Jonathan Reynolds, Alexander Melnikov, Natalia Lunova, and Orion Reblitz-Richardson · 2019
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An interpretable mortality prediction model for COVID-19 patients
Li Yan, Hai-Tao Zhang, Jorge Goncalves, Yang Xiao, Maolin Wang, Yuqi Guo, Chuan Sun, Xiuchuan Tang, Liang Jing, Mingyang Zhang, Xiang Huang, Ying Xiao, Haosen Cao, Yanyan Chen, Tongxin Ren, Fang Wang, Yaru Xiao, Sufang Huang, Xi Tan, Niannian Huang, Bo Jiao, Cheng Cheng, Yong Zhang, Ailin Luo, Laurent Mombaerts, Junyang Jin, Zhiguo Cao, Shusheng Li, Hui Xu, and Ye Yuan · 2020
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The immunology of COVID-19: is immune modulation an option for treatment?
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Disease state prediction from single-cell data using graph attention networks
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Benchmarking atlas-level data integration in single-cell genomics
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