How to reconstruct edges from autoencoder?

Hello,

I was working with the decoder of Autoencoder example, and wonder how to reconstruct a graph with decoder output. If I understood right, the decoder computes edge probabilities, and I got them like below.

tensor([0.6745, 0.6745, 0.6551, 0.6551, 0.6286, 0.6286, 0.6391, 0.6391, 0.5424,
        0.5424, 0.5542, 0.5542, 0.5694, 0.5694, 0.5375, 0.5375, 0.4936, 0.4936,
        0.6254, 0.6254, 0.5815, 0.5815, 0.6029, 0.6029, 0.5186, 0.5186, 0.4716,
        0.4716, 0.5158, 0.5158, 0.5058, 0.5058, 0.5641, 0.5641, 0.4994, 0.4994,
        0.5030, 0.5030, 0.6071, 0.6071, 0.6064, 0.6064, 0.5250, 0.5250, 0.5184,
        0.5184, 0.5531, 0.5531, 0.5415, 0.5415, 0.5445, 0.5445, 0.5138, 0.5138,
        0.5075, 0.5075, 0.4968, 0.4968, 0.5199, 0.5199, 0.4946, 0.4946, 0.5524,
        0.5524, 0.5587, 0.5587, 0.5585, 0.5585, 0.5088, 0.5088, 0.4806, 0.4806,
        0.5119, 0.5119, 0.5122, 0.5122, 0.5117, 0.5117, 0.5116, 0.5116, 0.5283,
        0.5283, 0.5211, 0.5211, 0.5121, 0.5121, 0.5273, 0.5273, 0.5119, 0.5119,
        0.5117, 0.5117, 0.4990, 0.4990, 0.4986, 0.4986, 0.5036, 0.5036, 0.5067,
        0.5067, 0.4918, 0.4918, 0.4983, 0.4983, 0.5210, 0.5210, 0.5012, 0.5012,
        0.5017, 0.5017, 0.5477, 0.5477, 0.5475, 0.5475, 0.4924, 0.4924, 0.5084,
        0.5084, 0.5098, 0.5098, 0.5256, 0.5256, 0.5719, 0.5719, 0.5012, 0.5012,
        0.5010, 0.5010, 0.5020, 0.5020, 0.5064, 0.5064, 0.5063, 0.5063, 0.5221,
        0.5221, 0.6704, 0.6704, 0.5566, 0.5566, 0.6233, 0.6233, 0.5059, 0.5059,
        0.5069, 0.5069, 0.5085, 0.5085, 0.5048, 0.5048, 0.5051, 0.5051, 0.5887,
        0.5887, 0.6524, 0.6524, 0.5295, 0.5295, 0.5474, 0.5474, 0.5241, 0.5241,
        0.5059, 0.5059, 0.5568, 0.5568, 0.5497, 0.5497, 0.5727, 0.5727, 0.5397,
        0.5397, 0.5805, 0.5805, 0.5577, 0.5577, 0.5569, 0.5569, 0.6282, 0.6282,
        0.6124, 0.6124, 0.6134, 0.6134, 0.5117, 0.5117, 0.4991, 0.4991, 0.5032,
        0.5032, 0.6867, 0.6867, 0.6140, 0.6140, 0.6222, 0.6222, 0.6541, 0.6541,
        0.6641, 0.6641, 0.7468, 0.7468, 0.7686, 0.7686, 0.6775, 0.6775, 0.7056,
        0.7056, 0.7104, 0.7104, 0.5137, 0.5137], grad_fn=<SigmoidBackward>)

of which length is 106. but my original data that I encoded has 212 edges and 117 nodes.

Data(edge_index=[2, 212], x=[117, 1])

How do I know which edge that each probability represents??

[rusty1s]

In addition, I highly suggest using the latest autoencoder example (with PyG from master). The old autoencoder had a bug, where test and val edges where not removed in the encoder.

[rusty1s]

Ah I see, so you are basically just using the autoencoder example with custom data. Actually, the split_edges function modifies the edge_index (and removes contrary edges), so the shapes are look okay to me (212//2=106).