Abstract
This paper studies the problem of learning representations for network. Existing approaches embed vertices into a low dimensional continuous space which encodes local or global network structures. While these methods show improvements over traditional representations on node classification tasks, they ignore label information until the learnt embeddings are used for training classifier. That is, the process of representation learning is separated from the labels and lacks such information.
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