Fetching the paper…
Reading the bibliography…
The recent advancements in graph neural networks (GNNs) have led to state-of-the-art performances in various applications, including chemo-informatics, question-answering systems, and recommender systems.
On Random Graphs I
Paul Erdős and Alfréd Rényi. 1959 · 1959
Earlier work this paper cites.
Probability Inequalities for Sums of Bounded Random Variables
Wassily Hoeffding. 1963 · 1963
Earlier work this paper cites.
Robust Characterizations of Polynomials with Applications to Program Testing
Ronitt Rubinfeld and Madhu Sudan. 1996 · 1996
Earlier work this paper cites.
A Neural Device for Searching Direct Correlations between Structures and Properties of Chemical Compounds
Igor I. Baskin, Vladimir A. Palyulin, and Nikolai S. Zefirov. 1997 · 1997
Earlier work this paper cites.
Supervised neural networks for the classification of structures
Alessandro Sperduti and Antonina Starita. 1997 · 1997
Earlier work this paper cites.
Emergence of Scaling in Random Networks
Albert-Laszlo Barabasi and Reka Albert. 1999 · 1999
Earlier work this paper cites.
A Sublinear Time Approximation Scheme for Clustering in Metric Spaces. In Proceedings of the 40th Annual Symposium on Foundations of Computer Science, FOCS . IEEE, 154–159
Piotr Indyk. 1999 · 1999
Earlier work this paper cites.
Sublinear time approximate clustering. In Proceedings of the Twelfth Annual Symposium on Discrete Algorithms, SODA . SIAM, USA, 439–447
Nina Mishra, Daniel Oblinger, and Leonard Pitt. 2001 · 2001
Earlier work this paper cites.
Estimating the weight of metric minimum spanning trees in sublinear-time. In Proceedings of the 36th Annual ACM Symposium on Theory of Computing, STOC . ACM, New York, NY, USA, 175–183
Artur Czumaj and Christian Sohler. 2004 · 2004
Earlier work this paper cites.
Approximating the Minimum Spanning Tree Weight in Sublinear Time
Bernard Chazelle, Ronitt Rubinfeld, and Luca Trevisan. 2005 · 2005
Earlier work this paper cites.
A new model for learning in graph domains. In Proceedings of the International Joint Conference on Neural Networks, IJCNN , Vol. 2. 729–734
Marco Gori, Gabriele Monfardini, and Franco Scarselli. 2005 · 2005
Earlier work this paper cites.
Approximating the minimum vertex cover in sublinear time and a connection to distributed algorithms
Michal Parnas and Dana Ron. 2007 · 2007
Earlier work this paper cites.
Constant-Time Approximation Algorithms via Local Improvements. In Proceedings of the 49th Annual Symposium on Foundations of Computer Science, FOCS . IEEE, 327–336
Huy N. Nguyen and Krzysztof Onak. 2008 · 2008
Cited alongside, same era.
The Graph Neural Network Model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009 · 2009
Cited alongside, same era.
An improved constant-time approximation algorithm for maximum matchings. In Proceedings of the 41st Annual ACM Symposium on Theory of Computing, STOC . ACM, New York, NY, USA, 225–234
Yuichi Yoshida, Masaki Yamamoto, and Hiro Ito. 2009 · 2009
Cited alongside, same era.
Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS . PMLR, 249–256
Xavier Glorot and Yoshua Bengio. 2010 · 2010
Cited alongside, same era.
Spectral Networks and Locally Connected Networks on Graphs. In 2nd International Conference on Learning Representations, ICLR
Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, NeurIPS . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
Deep Sets. In Advances in Neural Information Processing Systems 30, NeurIPS . 3391–3401
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander J. Smola. 2017 · 2017
Later among the works it cites.
Adaptive Sampling Towards Fast Graph Representation Learning. In Advances in Neural Information Processing Systems 31, NeurIPS
Wen-bing Huang, Tong Zhang, Yu Rong, and Junzhou Huang. 2018 · 2018
Later among the works it cites.
Modeling Relational Data with Graph Convolutional Networks. In The Semantic Web - 15th International Conference, ESWC . Springer, 593–607
Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 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…
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014 · 2014
Cited alongside, same era.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems 29, NeurIPS . 3837–3845
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Cited alongside, same era.
Minimizing Quadratic Functions in Constant Time. In Advances in Neural Information Processing Systems 29, NeurIPS . 2217–2225
Kohei Hayashi and Yuichi Yoshida. 2016 · 2016
Cited alongside, same era.
Neural Message Passing for Quantum Chemistry. In Proceedings of the 34th International Conference on Machine Learning, ICML . PMLR, 1263–1272
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017 · 2017
Cited alongside, same era.
Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30, NeurIPS . 1025–1035
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Fitting Low-Rank Tensors in Constant Time. In Advances in Neural Information Processing Systems 30, NeurIPS . 2470–2478
Kohei Hayashi and Yuichi Yoshida. 2017 · 2017
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the Fifth International Conference on Learning Representations, ICLR
Thomas N. Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In Proceedings of the Sixth International Conference on Learning Representations, ICLR
Jie Chen, Tengfei Ma, and Cao Xiao. 2018a
Cited in the paper.
Graph Attention Networks. In Proceedings of the Sixth International Conference on Learning Representations, ICLR
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Later among the works it cites.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD . ACM, New York, NY, USA, 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018 · 2018
Later among the works it cites.
An End-to-End Deep Learning Architecture for Graph Classification. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, AAAI . AAAI Press, USA, 4438–4445
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen. 2018 · 2018
Later among the works it cites.
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD . ACM, New York, NY, USA, 257–266
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019
Closest in time.
Graph Neural Networks for Social Recommendation. In The World Wide Web Conference, WWW . ACM, New York, NY, USA, 417–426
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Yihong Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Closest in time.
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD . ACM, New York, NY, USA, 596–606
Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, and Christos Faloutsos. 2019 · 2019
Closest in time.
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, and Quanquan Gu. 2019 · 2019
Closest in time.