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Erschienen in:
2019 | OriginalPaper | Buchkapitel
A Fast Approach of Graph Embedding Using Broad Learning System
verfasst von : Long Jiang, Yi Zuo, Tieshan Li, C. L. Philip Chen
Erschienen in: Multidisciplinary Social Networks Research
Verlag: Springer Singapore
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Abstract
In this paper, traditional DeepWalk method and broad learning system (BLS) are used to classify network nodes in graph embedding, and results of classification are compared. When categorizing, DeepWalk adopts one vs rest (OvR) logistic regression method, and BLS is applied after the production of vector representations. In order to obviously compare results of the two classification methods, Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are employed to carry out the experiment on multi-label classification of BlogCatalog. The experimental result shows that F1 score of BLS is obviously higher than DeepWalk and other methods, and training time of BLS is much less than other methods. These performances make our method suitable to graph embedding.