Browsing by Author "Suomela, Samu"

Large graphs often have labels only for a subset of nodes. Node classification is a semi-supervised learning task where unlabeled nodes are assigned labels utilizing the known information of the graph. In this thesis, three node classification methods are evaluated based on two metrics: computational speed and node classification accuracy. The three methods that are evaluated are label propagation, harmonic functions with Gaussian fields, and Graph Convolutional Neural Network (GCNN). Each method is tested on five citation networks of different sizes extracted from a large scientific publication graph, MAG240M-LSC. For each graph, the task is to predict the subject areas of scientific publications, e.g., cs.LG (Machine Learning). The motivation of the experiments is to give insight on whether the methods would be suitable for automatic labeling of scientific publications. The results show that label propagation and harmonic functions with Gaussian fields reach mediocre accuracy in the node classification task, while GCNN had a low accuracy. Label propagation was computationally slow compared to the other methods, whereas harmonic functions were exceptionally fast. Training of the GCNN took a long time compared to harmonic functions, but computational speed was acceptable. However, none of the methods reached a high enough classification accuracy to be utilized in automatic labeling of scientific publications.