A Bayesian graph embedding model for link-based classification problems

Zhang, Y., Zhuang, H., Liu, T., Chen, B. , Cao, Z., Fu, Y., Fan, Z. and Chen, G. (2022) A Bayesian graph embedding model for link-based classification problems. IEEE Transactions on Network Science and Engineering, 9(2), pp. 716-727. (doi: 10.1109/TNSE.2021.3131223)

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In recent years, the analysis of human interaction data has led to the rapid development of graph embedding methods. For link-based classification problems, topological information typically appears in various machine learning tasks in the form of embedded vectors or convolution kernels. This paper introduces a Bayesian graph embedding model for such problems, integrating network reconstruction, link prediction, and behavior prediction into a unified framework. Unlike the existing graph embedding methods, this model does not embed the topology of nodes or links into a low-dimensional space but sorts the probabilities of upcoming links and fuses the information of node topology and data domain via sorting. The new model integrates supervised transaction predictors with unsupervised link prediction models, summarizing local and global topological information. The experimental results on a financial trading dataset and a retweet network dataset demonstrate that the proposed feature fusion model outperforms the tested benchmarked machine learning algorithms in precision, recall, and F1-measure. The proposed learning structure has a fundamental methodological contribution and can be extended and applied to various link-based classification problems in different fields.

Item Type:Articles
Additional Information:This work was supported in part by the National Natural Science Foundation of China under Grant U1936205, Grant 61503285, Grant 61772367, and Grant 62172300, in part by the Municipal Natural Science Foundation of Shanghai under Grant 17ZR1446000 and Grant 21ZR1422000, in part by the China Postdoctoral Science Foundation under Grant 2020M670998, and in part by the Hong Kong Research Grants Council under the GRF Grant CityU 11206320.
Glasgow Author(s) Enlighten ID:Chen, Dr Bowei
Authors: Zhang, Y., Zhuang, H., Liu, T., Chen, B., Cao, Z., Fu, Y., Fan, Z., and Chen, G.
College/School:College of Social Sciences > Adam Smith Business School > Management
Journal Name:IEEE Transactions on Network Science and Engineering
ISSN (Online):2327-4697
Published Online:30 November 2021
Copyright Holders:Copyright © 2021 IEEE
First Published:First published in IEEE Transactions on Network Science and Engineering 9(2): 716-727
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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