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Graph Neural Networks have shown excellent performance on semi-supervisedclassification tasks. However, they assume access to a graph that may not beoften available in practice. In the absence of any graph, constructingk-Nearest Neighbor (kNN) graphs from the given data have shown to giveimprovements when used with GNNs over other semi-supervised methods. This paperproposes a semi-supervised graph learning method for cases when there are nographs available. This method learns a graph as a convex combination of theunsupervised kNN graph and a supervised label-affinity graph. Thelabel-affinity graph directly captures all the nodes' label-affinity with thelabeled nodes, i.e., how likely a node has the same label as the labeled nodes.This affinity measure contrasts with the kNN graph where the metric measurescloseness in the feature space. Our experiments suggest that this approachgives close to or better performance (up to 1.5%), while being simpler andfaster (up to 70x) to train, than state-of-the-art graph learning methods. Wealso conduct several experiments to highlight the importance of individualcomponents and contrast them with state-of-the-art methods.

GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networks