Node Label Classification Algorithm Based on Structural Depth Network Embedding Model
In the era of Internet,where massive data is growing explosively,traditional algorithms have been unable to meet the needs of processing large-scale and multi type data.In recent years,the latest graph embedding algorithm has achieved excellent results in link prediction,network reconstruction and n...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2022-03-01
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| Series: | Jisuanji kexue |
| Subjects: | |
| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-3-105.pdf |
| _version_ | 1828774501224022016 |
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| author | CHEN Shi-cong, YUAN De-yu, HUANG Shu-hua, YANG Ming |
| author_facet | CHEN Shi-cong, YUAN De-yu, HUANG Shu-hua, YANG Ming |
| author_sort | CHEN Shi-cong, YUAN De-yu, HUANG Shu-hua, YANG Ming |
| collection | DOAJ |
| description | In the era of Internet,where massive data is growing explosively,traditional algorithms have been unable to meet the needs of processing large-scale and multi type data.In recent years,the latest graph embedding algorithm has achieved excellent results in link prediction,network reconstruction and node classification by learning graph network characteristics.Based on the traditional automatic encoder model,a new algorithm combining Sdne algorithm and link prediction similarity matrix is proposed.By introducing a high-order loss function in the process of back-propagation,the performance is adjusted according to the new characteristics of the auto-encoder.The disadvantages of traditional algorithm in determining node similarity in a single way are improved.A simple model is established to analyze and prove the rationality of the optimization.Compared with the most effective Sdne algorithm in the latest research,the improvement effect of this algorithm on Micro-F1 and Macro-F1 two evaluation indicators is close to 1%,and the visual classification effect is good.At the same time,it is found that the optimal value of the hyperparameter of the higher-order loss function is approximately in the range of 1~10,and the change of the numerical value can basically maintain the robustness of the whole network. |
| first_indexed | 2024-12-11T15:22:22Z |
| format | Article |
| id | doaj.art-51b84092e6df47acb12bcb9070b9693e |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-12-11T15:22:22Z |
| publishDate | 2022-03-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-51b84092e6df47acb12bcb9070b9693e2022-12-22T01:00:20ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2022-03-0149310511210.11896/jsjkx.201000177Node Label Classification Algorithm Based on Structural Depth Network Embedding ModelCHEN Shi-cong, YUAN De-yu, HUANG Shu-hua, YANG Ming01 School of Information and Cyber Security,People's Public Security University of China,Beijing 100038,China <br/>2 Key Laboratory of Safety Precautions and Risk Assessment,Ministry of Public Security,Beijing 100038,ChinaIn the era of Internet,where massive data is growing explosively,traditional algorithms have been unable to meet the needs of processing large-scale and multi type data.In recent years,the latest graph embedding algorithm has achieved excellent results in link prediction,network reconstruction and node classification by learning graph network characteristics.Based on the traditional automatic encoder model,a new algorithm combining Sdne algorithm and link prediction similarity matrix is proposed.By introducing a high-order loss function in the process of back-propagation,the performance is adjusted according to the new characteristics of the auto-encoder.The disadvantages of traditional algorithm in determining node similarity in a single way are improved.A simple model is established to analyze and prove the rationality of the optimization.Compared with the most effective Sdne algorithm in the latest research,the improvement effect of this algorithm on Micro-F1 and Macro-F1 two evaluation indicators is close to 1%,and the visual classification effect is good.At the same time,it is found that the optimal value of the hyperparameter of the higher-order loss function is approximately in the range of 1~10,and the change of the numerical value can basically maintain the robustness of the whole network.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-3-105.pdfnetwork embedding|deep learning|node classification|auto-encoder|complex network |
| spellingShingle | CHEN Shi-cong, YUAN De-yu, HUANG Shu-hua, YANG Ming Node Label Classification Algorithm Based on Structural Depth Network Embedding Model Jisuanji kexue network embedding|deep learning|node classification|auto-encoder|complex network |
| title | Node Label Classification Algorithm Based on Structural Depth Network Embedding Model |
| title_full | Node Label Classification Algorithm Based on Structural Depth Network Embedding Model |
| title_fullStr | Node Label Classification Algorithm Based on Structural Depth Network Embedding Model |
| title_full_unstemmed | Node Label Classification Algorithm Based on Structural Depth Network Embedding Model |
| title_short | Node Label Classification Algorithm Based on Structural Depth Network Embedding Model |
| title_sort | node label classification algorithm based on structural depth network embedding model |
| topic | network embedding|deep learning|node classification|auto-encoder|complex network |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-3-105.pdf |
| work_keys_str_mv | AT chenshicongyuandeyuhuangshuhuayangming nodelabelclassificationalgorithmbasedonstructuraldepthnetworkembeddingmodel |