Zhang, Y;
Zhuang, H;
Liu, T;
Chen, B;
Cao, Z;
Fu, Y;
Fan, Z;
(2021)
A Bayesian graph embedding model for link-based classification problems.
IEEE Transactions on Network Science and Engineering
10.1109/TNSE.2021.3131223.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | A Bayesian graph embedding model for link-based classification problems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TNSE.2021.3131223 |
| Publisher version: | http://dx.doi.org/10.1109/TNSE.2021.3131223 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Prediction algorithms, Classification algorithms, Machine learning algorithms, Bayes methods, Topology, Predictive models, Task analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10142113 |
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