Can graph_conv_cheby method be applied to an arbitrary graph ?

[pinkfloyd06]

Hello,

Let me first thank you for your work.

l would like to ask you whether your graph_conv_cheby() can be applied to an arbitrary graph such as an irregular graph or just on a grid graph ?

Thank you

[mdeff]

Yes absolutely. You only need an adjacency matrix which describes your graph.

[pinkfloyd06]

@mdeff Thank you for your answer. Hoever l get confused after looking at your experiment on MNIST https://github.com/mdeff/cnn_graph/blob/master/nips2016/mnist.ipynb

A = grid_graph(28, corners=False)
and the number of edges per vertex is at least 8.

What if l don't have a grid. l deal with irregular graphs where each graph has N nodes such that :

each node_i has k_i vertices.

-So the number of vertices varies from node to another either in the same graph or on different graphs

• The number of nodes per graph is variable

[mdeff]

If the number of nodes per graph is variable, then your main issue is that you'll have to "summarize" the information in a fixed-length vector at some point. See the discussion in #5. As far as the graph convolution is concerned, you can have a different adjacency matrix for each data point.