2022

Disentangled Spatiotemporal Graph Generative Models

Du, Yuanqi, Guo, Xiaojie, Cao, Hengning et al.

Understand

Spatiotemporal graph represents a crucial data structure where the nodes and edges are embedded in a geometric space and can evolve dynamically over time.

Nowadays, spatiotemporal graph data is becoming increasingly popular and important, ranging from microscale (e.g.
protein folding), to middle-scale (e.g.
dynamic functional connectivity), to macro-scale (e.g.

Built on

Graph wavenet for deep spatial-temporal graph modeling
Original
Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C. 2019 · 1906
Earlier work this paper cites.
Urban transportation modeling and planning
Stopher, P. R.; and Meyburg, A. H. 1975 · 1975
Earlier work this paper cites.
The asymptotic number of labeled graphs with given degree sequences
Bender, E. A.; and Canfield, E. R. 1978 · 1978
Earlier work this paper cites.
A comparison of simulation approaches to epidemic modeling
Teng, P. 1985 · 1985
Earlier work this paper cites.
Routing of multipoint connections
Waxman, B. M. 1988 · 1988
Earlier work this paper cites.
Nonlinear programming
Mangasarian, O. L. 1994 · 1994
Earlier work this paper cites.
De novo protein design: fully automated sequence selection
Dahiyat, B. I.; and Mayo, S. L. 1997 · 1997
Earlier work this paper cites.
Mean-field theory for scale-free random networks
Barabási, A.-L.; Albert, R.; and Jeong, H. 1999 · 1999
Earlier work this paper cites.
Graphaf: a flow-based autoregressive model for molecular graph generation
Original
Shi, C.; Xu, M.; Zhu, Z.; Zhang, W.; Zhang, M.; and Tang, J. 2020 · 2001
Earlier work this paper cites.
Modeling the SARS epidemic
Dye, C.; and Gay, N. 2003 · 2003
Earlier work this paper cites.
Generating tertiary protein structures via an interpretative variational autoencoder
Original
Guo, X.; Du, Y.; Tadepalli, S.; Zhao, L.; and Shehu, A. 2020a · 2004
Earlier work this paper cites.
TG-GAN: Deep Generative Models for Continuously-time Temporal Graph Generation
Original
Zhang, L.; Zhao, L.; Qin, S.; and Pfoser, D. 2020 · 2005
Earlier work this paper cites.
SE (3)-transformers: 3D roto-translation equivariant attention networks
Original
Fuchs, F. B.; Worrall, D. E.; Fischer, V.; and Welling, M. 2020 · 2006
Earlier work this paper cites.
Giant component and connectivity in geographical threshold graphs
Bradonjić, M.; Hagberg, A.; and Percus, A. G. 2007 · 2007
Earlier work this paper cites.
Spatial networks
Barthélemy, M. 2011 · 2011
Earlier work this paper cites.
Big data and its technical challenges
Jagadish, H. V.; Gehrke, J.; Labrinidis, A.; Papakonstantinou, Y.; Patel, J. M.; Ramakrishnan, R.; and Shahabi, C. 2014 · 2014
Earlier work this paper cites.
Dynamic stochastic blockmodels for time-evolving social networks
Xu, K. S.; and Hero, A. O. 2014 · 2014
Earlier work this paper cites.
Modern temporal network theory: a colloquium
Holme, P. 2015 · 2015
Earlier work this paper cites.

Similar

Deep variational information bottleneck
Original
Alemi, A. A.; Fischer, I.; Dillon, J. V.; and Murphy, K. 2016 · 2016
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I.; Matthey, L.; and Pal, A. e. a. 2016 · 2016
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W. L.; Ying, R.; and Leskovec, J. 2017 · 2017
Cited alongside, same era.
Variational inference of disentangled latent concepts from unlabeled observations
Original
Kumar, A.; Sattigeri, P.; and Balakrishnan, A. 2017 · 2017
Cited alongside, same era.
Graph attention networks
Original
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2017 · 2017
Cited alongside, same era.
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Original
Yu, B.; Yin, H.; and Zhu, Z. 2017 · 2017
Cited alongside, same era.
Infovae: Information maximizing variational autoencoders
Original
Zhao, S.; Song, J.; and Ermon, S. 2017 · 2017
Cited alongside, same era.
Understanding disentangling in β \beta -VAE
Original
Burgess, C. P.; Higgins, I.; and Pal, A. e. a. 2018 · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Chen, R. T.; Li, X.; Grosse, R. B.; and Duvenaud, D. K. 2018 · 2018
Cited alongside, same era.
Deep graph translation
Original
Guo, X.; Wu, L.; and Zhao, L. 2018 · 2018
Cited alongside, same era.
Disentangling by factorising
Kim, H.; and Mnih, A. 2018 · 2018
Cited alongside, same era.
Information constraints on auto-encoding variational bayes
Original
Lopez, R.; Regier, J.; Jordan, M. I.; and Yosef, N. 2018 · 2018
Cited alongside, same era.

Then

Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F.; Bauer, S.; Lucic, M.; Raetsch, G.; Gelly, S.; Schölkopf, B.; and Bachem, O. 2019 · 2019
Later among the works it cites.
Disentangled graph convolutional networks
Ma, J.; Cui, P.; Kuang, K.; Wang, X.; and Zhu, W. 2019 · 2019
Later among the works it cites.
Mining temporal networks
Rozenshtein, P.; and Gionis, A. 2019 · 2019
Later among the works it cites.
Molecule property prediction based on spatial graph embedding
Wang, X.; Li, Z.; Jiang, M.; Wang, S.; Zhang, S.; and Wei, Z. 2019 · 2019
Later among the works it cites.
STGGAN: Spatial-temporal graph generation
Zhang, L. 2019 · 2019
Later among the works it cites.
Interpretable Molecule Generation via Disentanglement Learning
Du, Y.; Guo, X.; Shehu, A.; and Zhao, L. 2020 · 2020
Later among the works it cites.
Core: Automatic molecule optimization using copy & refine strategy
Fu, T.; Xiao, C.; and Sun, J. 2020 · 2020
Later among the works it cites.
Property Controllable Variational Autoencoder via Invertible Mutual Dependence
Guo, X.; Du, Y.; and Zhao, L. 2020 · 2020
Later among the works it cites.
A data-driven graph generative model for temporal interaction networks
Original
Zhou, D.; Zheng, L.; Han, J.; and He, J. 2020 · 2020
Later among the works it cites.
GraphGT: Machine Learning Datasets for Graph Generation and Transformation
Du, Y.; Wang, S.; Guo, X.; Cao, H.; Hu, S.; Jiang, J.; Varala, A.; Angirekula, A.; and Zhao, L. 2021b · 2021
Later among the works it cites.
Deep Latent-Variable Models for Controllable Molecule Generation
Du, Y.; Wang, Y.; Alam, F.; Lu, Y.; Guo, X.; Zhao, L.; and Shehu, A. 2021c · 2021
Later among the works it cites.
Disentangled Deep Generative model for Spatial Networks
Guo, X.; Du, Y.; and Zhao, L. 2021 · 2021
Later among the works it cites.
Generative models for graph-based protein design
Ingraham, J.; Garg, V. K.; Barzilay, R.; and Jaakkola, T. S. 2021 · 2021
Later among the works it cites.
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
Original
Liu, Z.; Chen, Y.; Du, Y.; and Tegmark, M. 2021 · 2021
Later among the works it cites.
Generative Adversarial Learning of Protein Tertiary Structures
Rahman, T.; Du, Y.; Zhao, L.; and Shehu, A. 2021 · 2021
Later among the works it cites.
Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network
Original
Roy, A.; Roy, K. K.; Ali, A. A.; Amin, M. A.; and Rahman, A. 2021 · 2021
Later among the works it cites.
OpenCDA: an open cooperative driving automation framework integrated with co-simulation
Xu, R.; Guo, Y.; Han, X.; Xia, X.; Xiang, H.; and Ma, J. 2021a · 2021
Later among the works it cites.
Event Prediction in the Big Data Era: A Systematic Survey
Zhao, L. 2021 · 2021
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…