A Unified Non-Negative Matrix Factorization Framework for Semi-Supervised Learning on Graphs

We propose a Semi-Supervised Learning (SSL) methodology that explicitly encodes different necessary priors to learn efficient representations for nodes in a network. The key to our framework is a semi-supervised cluster invariance constraint that explicitly groups nodes of similar labels together... (read more)

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