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To improve the classical network representation learning framework, a network representation learning model based on text attention mechanism optimization was proposed.The average embedding of context nodes was first learned. Then the average embedding of context nodes was used to introduce the attention mechanism. And the embedding of target nodes was determined by the attention and text embedding together. Attention mechanism was added to the text features to learn different weight values for words in the text features, so that the words that were beneficial to the model could get the maximum contribution and effectively avoid the influence of low-frequency words and noisy words on the model. Experiments on Citeseer(M10), DBLP(V4), and SDBLP datasets showed that the network node classification performance of this model was superior to DeepWalk algorithm and similar representation learning algorithms. In the network visualization analysis, the proposed algorithm had obvious clustering phenomenon and clustering boundary, and obtained the desired results.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2022156
China Classification Code:TP391.1
Citation Information:
[1]TANG Yanlong,YE Zhonglin,ZHAO Haixing ,et al.Network Representation Learning Model Based on Text Attention Mechanism Optimization[J].Journal of Zhengzhou University(Natural Science Edition),2023,55(06):41-47.DOI:10.13705/j.issn.1671-6841.2022156.
Fund Information:
国家重点研发计划项目(2020YFC1523300); 国家自然科学基金青年科学基金项目(62007019); 青海省自然科学基金青年项目(2021-ZJ-946Q)
2023-03-21
2023-03-21
2023-03-21