Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001 · 2001
Earlier work this paper cites.
Graphs over time: densification laws, shrinking diameters and possible explanations. In ACM SIGKDD . 177–187
Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2005 · 2005
Earlier work this paper cites.
Patterns and dynamics of users’ behavior and interaction: Network analysis of an online community
Pietro Panzarasa, Tore Opsahl, and Kathleen M Carley. 2009 · 2009
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
SNAP Datasets: Stanford Large Network Dataset Collection
Jure Leskovec and Andrej Krevl. 2014 · 2014
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Edge weight prediction in weighted signed networks. In ICDM . IEEE, 221–230
Srijan Kumar, Francesca Spezzano, VS Subrahmanian, and Christos Faloutsos. 2016 · 2016
Earlier work this paper cites.
The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh. 2016 · 2016
Earlier work this paper cites.
Learning Sparse Neural Networks through L _ 0 L\_0 Regularization
Christos Louizos, Max Welling, and Diederik P Kingma. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
Earlier work this paper cites.
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017 · 2017
Earlier work this paper cites.
Structured sequence modeling with graph convolutional recurrent networks. In ICONIP . Springer, 362–373
Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson. 2018 · 2018
Earlier work this paper cites.
Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen. 2018 · 2018
Cited alongside, same era.
Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour. 2019 · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan Eric Lenssen. 2019 · 2019
Cited alongside, same era.
Graph convolutional networks with eigenpooling. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining . 723–731
Yao Ma, Suhang Wang, Charu C Aggarwal, and Jiliang Tang. 2019 · 2019
Cited alongside, same era.
Xgnn: Towards model-level explanations of graph neural networks. In ACM SIGKDD . 430–438
Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji. 2020 · 2020
Later among the works it cites.
Gcn-se: Attention as explainability for node classification in dynamic graphs. In 2021 IEEE International Conference on Data Mining (ICDM) . IEEE, 1060–1065
Yucai Fan, Yuhang Yao, and Carlee Joe-Wong. 2021 · 2021
Later among the works it cites.
Pick and choose: a GNN-based imbalanced learning approach for fraud detection. In WWW . 3168–3177
Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He. 2021 · 2021
Later among the works it cites.
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. In ICLR
Sana Tonekaboni, Danny Eytan, and Anna Goldenberg. 2021 · 2021
Later among the works it cites.
On explainability of graph neural networks via subgraph explorations. In ICML . PMLR, 12241–12252
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji. 2021 · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Explainability methods for graph convolutional neural networks. In CVPR . 10772–10781
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann. 2019 · 2019
Cited alongside, same era.
Gnnexplainer: Generating explanations for graph neural networks. In NeurIPS . 9240–9251
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019 · 2019
Cited alongside, same era.
T-gcn: A temporal graph convolutional network for traffic prediction
Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. 2019 · 2019
Cited alongside, same era.
Heterogeneous similarity graph neural network on electronic health records. In Big Data . IEEE, 1196–1205
Zheng Liu, Xiaohan Li, Hao Peng, Lifang He, and S Yu Philip. 2020 · 2020
Cited alongside, same era.
Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020 · 2020
Cited alongside, same era.
Evolvegcn: Evolving graph convolutional networks for dynamic graphs. In AAAI , Vol. 34. 5363–5370
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, and Charles Leiserson. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
An explainer for temporal graph neural networks. In GLOBECOM . IEEE, 6384–6389
Wenchong He, Minh N Vu, Zhe Jiang, and My T Thai. 2022 · 2022
Later among the works it cites.
Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, and Yi Chang. 2022 · 2022
Later among the works it cites.
Reasoning over different types of knowledge graphs: Static, temporal and multi-modal
Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, and Fuchun Sun. 2022 · 2022
Later among the works it cites.
Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert. 2022 · 2022
Later among the works it cites.
Interpretable machine learning: Fundamental principles and 10 grand challenges
Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, and Chudi Zhong. 2022 · 2022
Later among the works it cites.
Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2022 · 2022
Later among the works it cites.
Explaining Dynamic Graph Neural Networks via Relevance Back-propagation
Jiaxuan Xie, Yezi Liu, and Yanning Shen. 2022 · 2022
Later among the works it cites.
Exploring the Whole Rashomon Set of Sparse Decision Trees. In NeurIPS
Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo Seltzer, and Cynthia Rudin. 2022 · 2022
Later among the works it cites.
ROLAND: graph learning framework for dynamic graphs. In ACM SIGKDD . 2358–2366
Jiaxuan You, Tianyu Du, and Jure Leskovec. 2022 · 2022
Later among the works it cites.
Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2022 · 2022
Later among the works it cites.
Deep Temporal Graph Clustering
Meng Liu, Yue Liu, Ke Liang, Siwei Wang, Sihang Zhou, and Xinwang Liu. 2023 · 2023
Closest in time.