Answers.Body: <p>I'm seeing recent trend of combining RNN/CNN with GNN(graph neural networks) so that both time dependency and topology are captured. I would suggest you to start by looking at DCRNN (Yaguang Li et al.), it's a strong baseline that everyone uses nowadays. Other good resources: </p>
  
  <ul>
  <li>Graph wavenet for deep spatial-temporal graph modeling (Zonghan Wu et al.)</li>
  <li>Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting (Bing Yu et al.)</li>
  <li>STANN: A Spatio–Temporal Attentive Neural Network for Traffic Prediction (Zhixiang He et al.) </li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-07-03T07:32:06.637Z
Answers.LastActivityDate: 2019-07-03T07:32:06.637Z
Answers.UserDisplayName: razvanc92
Body: <p>I want to study NN for time-varying directed graphs. However, as this field has developed relatively recently, it is difficult to find new ways. So the question is, is there any NN that can handle such data?</p>
  
Tags: <neural-networks><machine-learning><deep-learning><graphs><geometric-deep-learning>
Title: Is there a neural network method for time-varying directed graphs?
UserDisplayName: unsmoother
ThreadText: # Is there a neural network method for time-varying directed graphs?
  
  (Asked by: username_0 on 2019-07-03)
  
  I want to study NN for time-varying directed graphs. However, as this field has developed relatively recently, it is difficult to find new ways. So the question is, is there any NN that can handle such data?
  
  **Tags:** neural-networks, machine-learning, deep-learning, graphs, geometric-deep-learning
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-07-03. Score: 3 | Accepted answer
  
  I'm seeing recent trend of combining RNN/CNN with GNN(graph neural networks) so that both time dependency and topology are captured. I would suggest you to start by looking at DCRNN (Yaguang Li et al.), it's a strong baseline that everyone uses nowadays. Other good resources:
  
  * Graph wavenet for deep spatial-temporal graph modeling (Zonghan Wu et al.)
  * Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting (Bing Yu et al.)
  * STANN: A Spatio–Temporal Attentive Neural Network for Traffic Prediction (Zhixiang He et al.)
  
  
  ---
  
  