Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have shown great ability in modeling graph-structured data for various domains.
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Peter J Rousseeuw. 1987 · 1987
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality. In NeurIPS . 3111–3119
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014 · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks. In European conference on computer vision . Springer, 818–833
Matthew D Zeiler and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016a · 2016
Earlier work this paper cites.
Variational graph auto-encoders
Thomas N Kipf and Max Welling. 2016b · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs. In NeurIPS . 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision . 618–626
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017 · 2017
Earlier work this paper cites.
Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S Jaakkola. 2018 · 2018
Earlier work this paper cites.
Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao. 2018 · 2018
Earlier work this paper cites.
Towards explanation of dnn-based prediction with guided feature inversion. In SIGKDD . 1358–1367
Mengnan Du, Ninghao Liu, Qingquan Song, and Xia Hu. 2018 · 2018
Earlier work this paper cites.
Cayleynets: Graph convolutional neural networks with complex rational spectral filters
Ron Levie, Federico Monti, Xavier Bresson, and Michael M Bronstein. 2018 · 2018
Earlier work this paper cites.
Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 32
Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin. 2018 · 2018
Earlier work this paper cites.
Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Nicolas Papernot and Patrick McDaniel. 2018 · 2018
Earlier work this paper cites.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018 · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018a · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems. In SIGKDD . 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Simgnn: A neural network approach to fast graph similarity computation. In WSDM . 384–392
Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, and Wei Wang. 2019 · 2019
Cited alongside, same era.
Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour. 2019 · 2019
Simple and deep graph convolutional networks. In ICML . PMLR, 1725–1735
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020 · 2020
Later among the works it cites.
Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, Dawei Yin, and Yi Chang. 2020 · 2020
Later among the works it cites.
Parameterized Explainer for Graph Neural Network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020 · 2020
Later among the works it cites.
Gcc: Graph contrastive coding for graph neural network pre-training. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1150–1160
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
Later among the works it cites.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
This looks like that: deep learning for interpretable image recognition
Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su. 2019 · 2019
Cited alongside, same era.
Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. In SIGKDD . 257–266
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019
Cited alongside, same era.
TED: Teaching AI to explain its decisions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society . 123–129
Michael Hind, Dennis Wei, Murray Campbell, Noel CF Codella, Amit Dhurandhar, Aleksandra Mojsilović, Karthikeyan Natesan Ramamurthy, and Kush R Varshney. 2019 · 2019
Cited alongside, same era.
Deepgcns: Can gcns go as deep as cnns?. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 9267–9276
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. 2019 · 2019
Cited alongside, same era.
Explainability methods for graph convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10772–10781
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann. 2019 · 2019
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin. 2019 · 2019
Cited alongside, same era.
defend: Explainable fake news detection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 395–405
Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2020b · 2020
Later among the works it cites.
Semi-Supervised Graph-to-Graph Translation. In CIKM . 1863–1872
Tianxiang Zhao, Xianfeng Tang, Xiang Zhang, and Suhang Wang. 2020 · 2020
Later among the works it cites.
Self-supervised Training of Graph Convolutional Networks
Qikui Zhu, Bo Du, and Pingkun Yan. 2020 · 2020
Later among the works it cites.
Molecular generative Graph Neural Networks for Drug Discovery
Pietro Bongini, Monica Bianchini, and Franco Scarselli. 2021 · 2021
Later among the works it cites.
Towards Self-Explainable Graph Neural Network. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 302–311
Enyan Dai and Suhang Wang. 2021 · 2021
Later among the works it cites.
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
Dejun Jiang, Zhenxing Wu, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, Zhe Wang, Chao Shen, Dongsheng Cao, Jian Wu, and Tingjun Hou. 2021 · 2021
Later among the works it cites.
How to find your friendly neighborhood: Graph attention design with self-supervision. In International Conference on Learning Representations
Dongkwan Kim and Alice Oh. 2021 · 2021
Later among the works it cites.
On Explainability of Graph Neural Networks via Subgraph Explorations. In Proceedings of the 38th International Conference on Machine Learning (ICML) . 12241–12252
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji. 2021 · 2021
Later among the works it cites.
ProtGNN: Towards Self-Explaining Graph Neural Networks
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Cheekong Lee. 2021 · 2021
Later among the works it cites.