Answers.Body: <p>Most methods generalize naturally to undirected graphs, some of the simple examples are GCN [1], GIN [2] and GAT [3]. There are more sophisticated methods, but depending on the task, often they don't perform drastically different [4].</p>
  <p>It really depends on you specific task what works best for you. You might want to try some different architectures and I'd start with the GCN or GIN. You could opt for a nice library that allows benchmarking all the different architectures quite quickly like <a href="https://www.dgl.ai/" rel="nofollow noreferrer">deep graph library (DGL)</a> or <a href="https://pytorch-geometric.readthedocs.io/en/latest/" rel="nofollow noreferrer">Torch-Geometric</a>.</p>
  <ul>
  <li>[1] <a href="http://arxiv.org/abs/1609.02907" rel="nofollow noreferrer">Kipf and Welling (2017), Semi-Supervised Classification with Graph Convolutional Networks</a></li>
  <li>[2] <a href="http://arxiv.org/abs/1810.00826" rel="nofollow noreferrer">Xu et al. (2019), How Powerful are Graph Neural Networks?</a></li>
  <li>[3] <a href="http://arxiv.org/abs/1710.10903" rel="nofollow noreferrer">Veličković et al. (2018), Graph Attention Networks</a></li>
  <li>[4] <a href="https://www.kdd.in.tum.de/gnn-benchmark" rel="nofollow noreferrer">Shchur et al. (2021), Pitfalls of Graph Neural Network Evaluation</a></li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-12-15T15:31:50.387Z
Answers.Text: Thanks very much. This answer gives me clear guidance about what to do next.
Answers.UserDisplayName: han
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-12-15T15:45:29.963Z
Answers.Text: Nice, I'm glad I could help :)
Answers.UserDisplayName: Chillston
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-12-15T12:51:29.540Z
Answers.LastActivityDate: 2022-12-15T12:51:29.540Z
Answers.UserDisplayName: Chillston
Body: <p>I want to do a graph classification task. Those graphs are directed, and their edges have features. I knew little about graph representation methods, but I did some research, and find most of the works for graph classification seem to be based on undirected graphs. So is there a way to encode this kind of graph? Or any suggestions to encode this kind of graph?</p>
  
Tags: <neural-networks><graph-neural-networks>
Title: Is there a neural network method to encode a directed graph?
UserDisplayName: han
ThreadText: # Is there a neural network method to encode a directed graph?
  
  (Asked by: username_0 on 2022-12-15)
  
  I want to do a graph classification task. Those graphs are directed, and their edges have features. I knew little about graph representation methods, but I did some research, and find most of the works for graph classification seem to be based on undirected graphs. So is there a way to encode this kind of graph? Or any suggestions to encode this kind of graph?
  
  **Tags:** neural-networks, graph-neural-networks
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-12-15. Score: 1
  
  Most methods generalize naturally to undirected graphs, some of the simple examples are GCN [1], GIN [2] and GAT [3]. There are more sophisticated methods, but depending on the task, often they don't perform drastically different [4].
  
  It really depends on you specific task what works best for you. You might want to try some different architectures and I'd start with the GCN or GIN. You could opt for a nice library that allows benchmarking all the different architectures quite quickly like [deep graph library (DGL)](https://www.dgl.ai/) or [Torch-Geometric](https://pytorch-geometric.readthedocs.io/en/latest/).
  
  * [1] [Kipf and Welling (2017), Semi-Supervised Classification with Graph Convolutional Networks](http://arxiv.org/abs/1609.02907)
  * [2] [Xu et al. (2019), How Powerful are Graph Neural Networks?](http://arxiv.org/abs/1810.00826)
  * [3] [Veličković et al. (2018), Graph Attention Networks](http://arxiv.org/abs/1710.10903)
  * [4] [Shchur et al. (2021), Pitfalls of Graph Neural Network Evaluation](https://www.kdd.in.tum.de/gnn-benchmark)
  
  > **Comments:**
  > **username_0**: Thanks very much. This answer gives me clear guidance about what to do next.  
  > **username_1**: Nice, I'm glad I could help :)  
  
  ---
  
  