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Very interesting! It is unfortunate that you could not expect more representational power theoretically, but perhaps personalizing using conditional information

Very interesting! It is unfortunate that you could not expect more representational power theoretically, but perhaps personalizing using conditional information is easier in this framework vs. the standard linalg approach. I have been having hopes for "network translation" specifically using encode-decode LSTM networks, or learning a generative model of a given network using unsupervised enc-dec, and trying to find connections which should be there but are not. May work for recommendations and/or suggesting "friend" connections if I am thinking about it correctly. It would be architecturally similar to the recent video work in [1] Thanks for the link to your paper, I plan on reading it in more detail soon. [1] http://arxiv.org/pdf/1502.04681v1.pdf http://arxiv.org/pdf/1502.04681v1.pdf

thashim 12y ago
The last sentence of your post is known as 'link-prediction' in the networks literature. [1] is a pretty comprehensive survey from the networks perspective. In many cases this reduces to learning pairwise distances between vertices. The network translation view is interesting, though one caution would be that you can have two graphs constructed on identical points, but with a different graph construction technique (say k-nearest neighbor vs fixed eps ball graph) and it is unclear if you should say the two graphs are the same (since they share the same latent coordinates) or if they are different since the graphs differ. The generative model approach seems like a pretty nice way to go since it lets you evaluate against simulation and ground truth. The only problem in my view is that network models may be quite limited since the relevant network models for these problems rely on exchangability of some type. We've a paper in review on the link prediction problem via random walk hitting times as a similarity measure. We don't have a preprint on Arxiv since that would de-anonymize us, but I'd be happy to send a copy if this is relevant to your work. [1] http://arxiv.org/abs/1010.0725 http://arxiv.org/abs/1010.0725

thashim 12y ago

The last sentence of your post is known as 'link-prediction' in the networks literature. [1] is a pretty comprehensive survey from the networks perspective. In many cases this reduces to learning pairwise distances between vertices. The network translation view is interesting, though one caution would be that you can have two graphs constructed on identical points, but with a different graph construction technique (say k-nearest neighbor vs fixed eps ball graph) and it is unclear if you should say the two graphs are the same (since they share the same latent coordinates) or if they are different since the graphs differ. The generative model approach seems like a pretty nice way to go since it lets you evaluate against simulation and ground truth. The only problem in my view is that network models may be quite limited since the relevant network models for these problems rely on exchangability of some type. We've a paper in review on the link prediction problem via random walk hitting times as a similarity measure. We don't have a preprint on Arxiv since that would de-anonymize us, but I'd be happy to send a copy if this is relevant to your work. [1] http://arxiv.org/abs/1010.0725 http://arxiv.org/abs/1010.0725