Graph Learning Network: A Structure Learning Algorithm

Darwin Saire Pilco; Adín Ramírez Rivera

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arXiv · 1905.12665

Graph Learning Network: A Structure Learning Algorithm

Darwin Saire Pilco·Adín Ramírez Rivera

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Abstract

Recently, graph neural networks (GNNs) have proved to be suitable in tasks on unstructured data. Particularly in tasks as community detection, node classification, and link prediction. However, most GNN models still operate with static relationships. We propose the Graph Learning Network (GLN), a simple yet effective process to learn node embeddings and structure prediction functions. Our model uses graph convolutions to propose expected node features, and predict the best structure based on them. We repeat these steps recursively to enhance the prediction and the embeddings.

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Darwin Saire Pilco, Adín Ramírez Rivera. 2019-05-29. Graph Learning Network: A Structure Learning Algorithm. https://arxiv.org/abs/1905.12665

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