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I'd like to test a new algorithm for link prediction on graphs. I'm interested in both weighted/unweighted and directed/undirected graphs.

What are some common benchmark datasets that researchers often use to test their algorithms? I know that within Computer Vision people often use MNIST or CIFAR, but I haven't found similar datasets for link prediction.

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A cursory Google search gave me:

Another way would be to create your own dataset by downloading say Github user's graph, defining a network by adding edges between user A and user B if A follows B and so on. Then you can take snapshots of this graph at different time instances and see if you can use last t snapshots to predict the edges in the t+1th snapshot. Of course that would be a lot more extra effort and the links I posted may suffice. Please let me know if it helps.

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