Abstract
In the fields of social network analysis and knowledge graph, many semi-supervised learning algorithms based on graph convolutional neural network (GCN) have been widely used. Most of these algorithms usually improve the structure of the neural network and the sampling method of each layer of the neural network. However, they don’t pay much attention to the data pre-processing of the algorithm. In the analysis of the input data, the words of different quality in these original data are unevenly distributed. This may obscure some useful data and highlight some irrelevant data. In order to verify the correctness of this hypothesis, the paper proposes a feature matrix compression algorithm (FMC algorithm) for data pre-processing of GCN-based algorithms. The algorithm analyzes and arranges the word columns of the input matrix (the feature of graph) according to the frequency of the word, then merges those words in which the word frequency is smaller, so as to emphasize the role of these words in the graph and optimize the data scale. The present work uses four mainstream datasets in the field and several representative and different algorithms to complete the experiment. The experimental results show that the FMC algorithm achieves better performance.
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Acknowledgements
The authors wish to thank the anonymous reviewers for their detailed feedback and suggestions for improving this work.
Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61272209, 61872164), in part by the Program of Science and Technology Development Plan of Jilin Province of China under Grant 20190302032GX, and in part by the Fundamental Research Funds for the Central Universities (Jilin University).
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Wang, H., Dong, LY., Ma, XT. et al. A Graph Attribute Aggregation Method based on Feature Engineering. J. Inst. Eng. India Ser. B 103, 711–719 (2022). https://doi.org/10.1007/s40031-021-00698-z
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DOI: https://doi.org/10.1007/s40031-021-00698-z