Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data.
Multivariate point processes
PAW Lewis · 1972
Earlier work this paper cites.
Cluster detection methods applied to the upper cape cod cancer data
Al Ozonoff, Thomas Webster, Veronica Vieira, Janice Weinberg, David Ozonoff, and Ann Aschengrau · 2005
Earlier work this paper cites.
A comparison of spatial clustering and cluster detection techniques for childhood leukemia incidence in ohio, 1996–2003
David C Wheeler · 2007
Earlier work this paper cites.
Random features for large-scale kernel machines
A. Rahimi and B. Recht · 2008
Earlier work this paper cites.
Spatial cluster analysis of early stage breast cancer: a method for public health practice using cancer registry data
Jaymie R Meliker, Geoffrey M Jacquez, Pierre Goovaerts, Glenn Copeland, and May Yassine · 2009
Earlier work this paper cites.
Aberrant frontal and temporal complex network structure in schizophrenia: A graph theoretical analysis
M.P. Van Den Heuvel, R.C.W. Mandl, C.J. Stam., R.S. Kahn, P. Hulshoff, and E. Hilleke · 2010
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio · 2014
Earlier work this paper cites.
A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science
J. H. Faghmous and V. Kumar · 2014
Earlier work this paper cites.
Spatio-temporal analytics for exploring human mobility patterns and urban dynamics in the mobile age
S. Gao · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
T.N. Kipf and M. Welling · 2016
Earlier work this paper cites.
Variational graph auto-encoders
T.N. Kipf and M. Welling · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
Earlier work this paper cites.
Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
Earlier work this paper cites.
M. Raissi, P. Perdikaris, and G.E. Karniadakis · 2017
Earlier work this paper cites.
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song · 2017
Earlier work this paper cites.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2017
Earlier work this paper cites.
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
B. Yu, H. Yin, and Z. Zhu · 2017
Earlier work this paper cites.
Weisfeiler-lehman neural machine for link prediction
Muhan Zhang and Yixin Chen · 2017
Earlier work this paper cites.
DeepEye: Link prediction in dynamic networks based on non-negative matrix factorization
N.M. Ahmed, L. Chen, Y. Wang, B. Li, Y. Li, and W. Li · 2018
Earlier work this paper cites.
Network-based assessment of the vulnerability of italian regions to bovine brucellosis
Alexandre Darbon, Eugenio Valdano, Chiara Poletto, Armando Giovannini, Lara Savini, Luca Candeloro, and Vittoria Colizza · 2018
Earlier work this paper cites.
Hyte: Hyperplane-based temporally aware knowledge graph embedding
Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha Talukdar · 2018
Earlier work this paper cites.
Epidemics on dynamic networks
J. Enright and R.K. Rowland · 2018
Earlier work this paper cites.
Large-scale learnable graph convolutional networks
Hongyang Gao, Zhengyang Wang, and Shuiwang Ji · 2018
Earlier work this paper cites.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Earlier work this paper cites.
Dyngem: Deep embedding method for dynamic graphs
Palash Goyal, Nitin Kamra, Xinran He, and Yan Liu · 2018
Earlier work this paper cites.
Learning dynamic embeddings from temporal interactions
Srijan Kumar, Xikun Zhang, and Jure Leskovec · 2018
Earlier work this paper cites.
How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2018
Earlier work this paper cites.
Semi-implicit variational inference
M. Yin and M. Zhou · 2018
Earlier work this paper cites.
Link prediction based on graph neural networks
M. Zhang and Y. Chen · 2018
Earlier work this paper cites.
Disease persistence on temporal contact networks accounting for heterogeneous infectious periods
Alexandre Darbon, Davide Colombi, Eugenio Valdano, Lara Savini, Armando Giovannini, and Vittoria Colizza · 2019
Earlier work this paper cites.
Learning dynamic context graphs for predicting social events
S. Deng, H. Rangwala, and Y. Ning · 2019
Earlier work this paper cites.
Graph neural networks for social recommendation
W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, and D. Yin · 2019
Earlier work this paper cites.
Variational graph recurrent neural networks
E. Hajiramezanali, A. Hasanzadeh, K. Narayanan, N. Duffield, M. Zhou, and X. Qian · 2019
Earlier work this paper cites.
Exposure reconstruction using space-time information technology
GM Jacquez, JR Meliker, RR Rommel, and PE Goovaerts · 2019
Earlier work this paper cites.
Contact-based model for epidemic spreading on temporal networks
Andreas Koher, Hartmut HK Lentz, James P Gleeson, and Philipp Hövel · 2019
Earlier work this paper cites.
Weisfeiler and Leman go neural: Higher-order graph neural networks
C. Morris, M. Ritzert, M. Fey, W.L. Hamilton, J.E. Lenssen, G. Rattan, and M. Grohe · 2019
Cited alongside, same era.
Spatio-temporal deep graph infomax
F. L. Opolka, A. Solomon, C. Cangea, P. Veličković, P. Liò, and R.D. Hjelm · 2019
Cited alongside, same era.
Exploring customer data using spatio-temporal analysis: Case study of fixed broadband provider
Asma Rosyidah, Isti Surjandari, et al · 2019
Cited alongside, same era.
Dyane: dynamics-aware node embedding for temporal networks
Koya Sato, Mizuki Oka, Alain Barrat, and Ciro Cattuto · 2019
Cited alongside, same era.
Learning to represent the evolution of dynamic graphs with recurrent models
Aynaz Taheri, Kevin Gimpel, and Tanya Berger-Wolf · 2019
Cited alongside, same era.
Node classification in temporal graphs through stochastic sparsification and temporal structural convolution
Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang · 2021
Later among the works it cites.
Global explainability of GNNs via logic combination of learned concepts
S. Azzolin, A. Longa, P. Barbiero, P. Liò, and A. Passerini · 2022
Later among the works it cites.
S. Beddar-Wiesing, G.A. D’Inverno, C. Graziani, V. Lachi, A. Moallemy-Oureh, F. Scarselli, and J.M. Thomas · 2022
Later among the works it cites.
Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in GNNs
C. Bodnar, F. Di Giovanni, B.P. Chamberlain, P. Liò, and M. Bronstein · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dyrep: Learning representations over dynamic graphs
R. Trivedi, M. Farajtabar, P. Biswal, and H. Zha · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
Cited alongside, same era.
Spatio-temporal attentive rnn for node classification in temporal attributed graphs
Dongkuan Xu, Wei Cheng, Dongsheng Luo, Xiao Liu, and Xiang Zhang · 2019
Cited alongside, same era.
Gnnexplainer: Generating explanations for graph neural networks
Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec · 2019
Cited alongside, same era.
A multi-scale approach for graph link prediction
Lei Cai and Shuiwang Ji · 2020
Cited alongside, same era.
Open Graph Benchmark: Datasets for machine learning on graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2020
Cited alongside, same era.
Representation learning for dynamic graphs: A survey
S.M. Kazemi, R. Goel, K. Jain, I. Kobyzev, A. Sethi, P. Forsyth, and P. Poupart · 2020
Cited alongside, same era.
A. Cini, I. Marisca, F.M. Bianchi, and C. Alippi · 2022
Later among the works it cites.
Scientific machine learning through physics–informed neural networks: where we are and what’s next
S. Cuomo, V.S. Di Cola, F. Giampaolo, G. Rozza, M. Raissi, and F Piccialli · 2022
Later among the works it cites.
Graph neural networks for recommender system
C. Gao, X. Wang, X. He, and Y. Li · 2022
Later among the works it cites.
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
H. Gao, M.J. Zahr, and J. Wang · 2022
Later among the works it cites.
Detecting the critical states during disease development based on temporal network flow entropy
R. Gao, J. Yan, P. Li, and L. Chen · 2022
Later among the works it cites.
DynaGraph: dynamic graph neural networks at scale
M. Guan, A.P. Iyer, and T. Kim · 2022
Later among the works it cites.
A survey on temporal graph representation learning and generative modeling
S. Gupta and S. Bedathur · 2022
Later among the works it cites.
An explainer for temporal graph neural networks
W. He, M. N. Vu, Z. Jiang, and M. T. Thai · 2022
Later among the works it cites.
Disease spreading modeling and analysis: A survey
Pietro Hiram Guzzi, Francesco Petrizzelli, and Tommaso Mazza · 2022
Later among the works it cites.
Graph neural network for traffic forecasting: A survey
Weiwei Jiang and Jiayun Luo · 2022
Later among the works it cites.
Forecasting global weather with graph neural networks
R. Keisler · 2022
Later among the works it cites.
Analysis of complex customer networks: A real-world banking example
Karmela Ljubičić, Andro Merćep, and Zvonko Kostanjčar · 2022
Later among the works it cites.
Explaining the explainers in graph neural networks: a comparative study
A. Longa, S. Azzolin, G. Santin, G. Cencetti, P. Liò, B. Lepri, and A. Passerini · 2022
Later among the works it cites.
Neighbourhood matching creates realistic surrogate temporal networks
A. Longa, G. Cencetti, S. Lehmann, A. Passerini, and B. Lepri · 2022
Later among the works it cites.
An efficient procedure for mining egocentric temporal motifs
A. Longa, G. Cencetti, B. Lepri, and A. Passerini · 2022
Later among the works it cites.
Neighborhood-aware scalable temporal network representation learning
Y. Luo and P. Li · 2022
Later among the works it cites.
Generating mobility networks with generative adversarial networks
G. Mauro, M. Luca, A. Longa, B. Lepri, and L. Pappalardo · 2022
Later among the works it cites.
Earthquake location and magnitude estimation with graph neural networks
I.W. McBrearty and G.C. Beroza · 2022
Later among the works it cites.
Discrete-time dynamic graph echo state networks
A. Micheli and D. Tortorella · 2022
Later among the works it cites.
Temporal link prediction: A unified framework, taxonomy, and review
M. Qin and D Yeung · 2022
Later among the works it cites.
Provably expressive temporal graph networks
A.H. Souza, D. Mesquita, S. Kaski, and V. Garg · 2022
Later among the works it cites.
Graph neural networks designed for different graph types: A survey
Josephine M Thomas, Alice Moallemy-Oureh, Silvia Beddar-Wiesing, and Clara Holzhüter · 2022
Later among the works it cites.
On the limit of explaining black-box temporal graph neural networks
M. N. Vu and M. T. Thai · 2022
Later among the works it cites.
Integrating scientific knowledge with machine learning for engineering and environmental systems
J. Willard, X. Jia, S. Xu, M. Steinbach, and V. Kumar · 2022
Later among the works it cites.
Graph neural networks in recommender systems: a survey
S. Wu, F. Sun, W. Zhang, X. Xie, and B. Cui · 2022
Later among the works it cites.
Dynamic network embedding survey
G. Xue, M. Zhong, J. Li, J. Chen, C. Zhai, and R. Kong · 2022
Later among the works it cites.
ROLAND: graph learning framework for dynamic graphs
J. You, T. Du, and J. Leskovec · 2022
Later among the works it cites.
TGL: A general framework for temporal GNN training on billion-scale graphs
H. Zhou, D. Zheng, I. Nisa, V. Ioannidis, X. Song, and G. Karypis · 2022
Later among the works it cites.
The expressive power of pooling in graph neural networks
F.M. Bianchi and V. Lachi · 2023
Closest in time.
Temporal network analysis using zigzag persistence
Audun Myers, David Muñoz, Firas A Khasawneh, and Elizabeth Munch · 2023
Closest in time.
Inferring patient zero on temporal networks via graph neural networks
Xiaolei Ru, Jack Murdoch Moore, Xin-Ya Zhang, Yeting Zeng, and Gang Yan · 2023
Closest in time.