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DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series

arXiv 2203.15009 ↗First posted 28 Mar 2022stat.ML cs.LG q-fin.STcited by 0

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Abstract (arXiv)

Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based dynamics are core objects of study. Designing flexible and scalable generative models is a very challenging task due to the high dimensionality of the data, as well as the need to represent temporal dependencies and marginal network structure. Here we introduce DAMNETS, a scalable deep generative model for network time series. DAMNETS outperforms competing methods on all of our measures of sample quality, over both real and synthetic data sets.

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21 of 45 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2017Graph Attention Networkscited by 8,854
  6. 2017Inductive Representation Learning on Large Graphscited by 6,284
  7. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  8. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  9. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  10. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  11. 2018GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Modelscited by 406
  12. 2015Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networkscited by 305
9 more
  1. 2019Efficient Graph Generation with Graph Recurrent Attention Networkscited by 231
  2. 2018Learning Deep Generative Models of Graphscited by 227
  3. 2019Variational Graph Recurrent Neural Networkscited by 86
  4. 2020Scalable Deep Generative Modeling for Sparse Graphscited by 63
  5. 2019Graph Kernels: A Surveycited by 43
  6. 2014Measuring large-scale social networks with high resolutioncited by 6
  7. 2019Autoregressive Models for Sequences of Graphscited by 4
  8. 2016Optimal Containment of Epidemics in Temporal and Adaptive Networkscited by 2
  9. 2019In Defense of Synthetic Datacited by 2

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