Nowadays, graph-based semi-supervised learning (GB-SSL) is a fast-growing area of classifying nodes in a graph with an extremely low number of labelled nodes. However, the GB-SSL algorithms have two general limitations: the first is the memory/time complexity that arises in all state-of-the-art GB-SSL algorithms on extremely large graphs. In...
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December 1, 2022 (v1)PublicationUploaded on: March 25, 2023
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October 7, 2020 (v1)Conference paper
Nowadays, Semi-Supervised Learning (SSL) on citation graph data sets is a rapidly growing area of research. However, the recently proposed graph-based SSL algorithms use a default adjacency matrix with binary weights on edges (citations), that causes a loss of the nodes (papers) similarity information. In this work, therefore, we propose a...
Uploaded on: December 4, 2022 -
June 20, 2021 (v1)Conference paper
A novel framework called Graph Diffusion & PCA (GDPCA) is proposed in the context of semi-supervised learning on graph structured data. It combines a modified Principal Component Analysis with the classical supervised loss and Laplacian regularization, thus handling the case where the adjacency matrix is sparse and avoiding the curse of...
Uploaded on: December 3, 2022