Even though my data is undirectional graph can the explainer still give directed graph exaplantion #9261
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bojack-horseman91
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We visualize a directed graph since the scores for an edge may still be different depending on its direction. Imagine you have a one-layer GNN, then you would expect that all outgoing edges from a seed node have importance score ~ 0 (since they do not contribute to the final output), while incoming edges have an importance score > 0. |
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So for interpretation purpose how do we percieve these directions? Because
my edges are representing relationship between two genes how do i make
inference from the direction of the graph ?
…On Thu, 2 May, 2024, 6:55 pm Matthias Fey, ***@***.***> wrote:
We visualize a directed graph since the scores for an edge may still be
different depending on its direction. Imagine you have a one-layer GNN,
then you would expect that all outgoing edges from a seed node have
importance score ~ 0 (since they do not contribute to the final output),
while incoming edges have an importance score > 0.
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Well I was hoping to get some biological inference because I had set the
edge attribute to be co-methylation ,which is a fancy way of saying
correlation between two genes. How do i interpet the direction in a
biological terms?
…On Fri, 3 May, 2024 at 6:46 PM, Matthias Fey ***@***.***> wrote:
I would just interpret them as information flow. One edge updates node A
by information from node B, and one edge updates node B by information from
node A.
how do i make inference from the direction of the graph ?
Can you clarify? I don't understand yet.
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I have checked that all my data is undirectional using is_directed() function and found none are directional and yet when I use GNNExxplainer to produce an explanation I get a directed graph visualized when I run explanation.get_explanation_subgraph().visualize_graph("path.png"). Can you tell me what's wrong?
P.S: I have used PNAConv as my GNN then feed it to a Fully connected layer
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