Graph regularization methods
Shallow embedding methods described in the previous section show how topological information and relations between data points can be encoded and leveraged in order to build more robust classifiers and address semi-supervised tasks. In general terms, network information can be extremely useful in constraining models and enforcing the output to be smooth within neighboring nodes. As we have already seen in previous sections, this idea can be efficiently used in semi-supervised tasks, when propagating the information on neighbor unlabeled nodes.
On the other hand, this can also be used to regularize the learning phase in order to create more robust models that tend to better generalize to unseen examples. Both the label propagation and the label spreading algorithms we have seen previously can be implemented as a cost function to be minimized when we add an additional regularization term. Generally, in supervised tasks, we can write the cost function...
