DGSLN: Differentiable graph structure learning neural network for robust graph representations

[1]  Kuan-Ching Li,et al.  Efficient Distributed Approaches to Core Maintenance on Large Dynamic Graphs , 2022, IEEE Transactions on Parallel and Distributed Systems.

[2]  Kevin Swersky,et al.  Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks , 2021, 2022 IEEE International Conference on Data Mining (ICDM).

[3]  Shuiwang Ji,et al.  Graph U-Nets , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Bo Zong,et al.  Learning to Drop: Robust Graph Neural Network via Topological Denoising , 2020, WSDM.

[5]  Cen Chen,et al.  Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks , 2020, ACM Trans. Knowl. Discov. Data.

[6]  Qian Xu,et al.  Graph Random Neural Networks for Semi-Supervised Learning on Graphs , 2020, NeurIPS.

[7]  Suhang Wang,et al.  Graph Structure Learning for Robust Graph Neural Networks , 2020, KDD.

[8]  Ruohong Zhang,et al.  Graph-Revised Convolutional Network , 2019, ECML/PKDD.

[9]  Guido Montúfar,et al.  Haar Graph Pooling , 2019, ICML.

[10]  Cesare Alippi,et al.  Spectral Clustering with Graph Neural Networks for Graph Pooling , 2019, ICML.

[11]  Wei Hu,et al.  Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification , 2019, 2020 IEEE International Conference on Multimedia and Expo (ICME).

[12]  Jiajun Bu,et al.  Hierarchical Graph Pooling with Structure Learning , 2019, AAAI 2020.

[13]  Stephan Günnemann,et al.  Diffusion Improves Graph Learning , 2019, NeurIPS.

[14]  Jure Leskovec,et al.  Improving Graph Attention Networks with Large Margin-based Constraints , 2019, ArXiv.

[15]  Renjie Liao,et al.  Efficient Graph Generation with Graph Recurrent Attention Networks , 2019, NeurIPS.

[16]  Bin Luo,et al.  Semi-Supervised Learning With Graph Learning-Convolutional Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Yoshua Bengio,et al.  GMNN: Graph Markov Neural Networks , 2019, ICML.

[18]  Charu C. Aggarwal,et al.  Graph Convolutional Networks with EigenPooling , 2019, KDD.

[19]  Jaewoo Kang,et al.  Self-Attention Graph Pooling , 2019, ICML.

[20]  Massimiliano Pontil,et al.  Learning Discrete Structures for Graph Neural Networks , 2019, ICML.

[21]  Jiaqi Wang,et al.  Three-way enhanced convolutional neural networks for sentence-level sentiment classification , 2019, Inf. Sci..

[22]  Kilian Q. Weinberger,et al.  Simplifying Graph Convolutional Networks , 2019, ICML.

[23]  Jure Leskovec,et al.  How Powerful are Graph Neural Networks? , 2018, ICLR.

[24]  Stefano Ermon,et al.  Graphite: Iterative Generative Modeling of Graphs , 2018, ICML.

[25]  Nathanael Perraudin,et al.  Large Scale Graph Learning from Smooth Signals , 2017, ICLR.

[26]  Philip S. Yu,et al.  A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[27]  Pietro Liò,et al.  Towards Sparse Hierarchical Graph Classifiers , 2018, ArXiv.

[28]  Jure Leskovec,et al.  Hierarchical Graph Representation Learning with Differentiable Pooling , 2018, NeurIPS.

[29]  Yixin Chen,et al.  An End-to-End Deep Learning Architecture for Graph Classification , 2018, AAAI.

[30]  Jure Leskovec,et al.  GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models , 2018, ICML.

[31]  Ruoyu Li,et al.  Adaptive Graph Convolutional Neural Networks , 2018, AAAI.

[32]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[33]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[34]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[35]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[36]  Ramón Fernández Astudillo,et al.  From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification , 2016, ICML.

[37]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Honglak Lee,et al.  Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.

[39]  Feiping Nie,et al.  Clustering and projected clustering with adaptive neighbors , 2014, KDD.

[40]  Pascal Frossard,et al.  The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.

[41]  W. Kittisupamongkol.  Two sides of the same coin? , 2010, Singapore medical journal.

[42]  Lise Getoor,et al.  Collective Classification in Network Data , 2008, AI Mag..