Coarsening effects on k-partite network classification
Abstract The growing data size poses challenges for storage and computational processing time in semi-supervised models, making their practical application difficult; researchers have explored the use of reduced network versions as a potential solution. Real-world networks contain diverse types of v...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-12-01
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| Series: | Applied Network Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41109-023-00606-y |
| _version_ | 1827590709052440576 |
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| author | Paulo Eduardo Althoff Alan Demétrius Baria Valejo Thiago de Paulo Faleiros |
| author_facet | Paulo Eduardo Althoff Alan Demétrius Baria Valejo Thiago de Paulo Faleiros |
| author_sort | Paulo Eduardo Althoff |
| collection | DOAJ |
| description | Abstract The growing data size poses challenges for storage and computational processing time in semi-supervised models, making their practical application difficult; researchers have explored the use of reduced network versions as a potential solution. Real-world networks contain diverse types of vertices and edges, leading to using k-partite network representation. However, the existing methods primarily reduce uni-partite networks with a single type of vertex and edge. We develop a new coarsening method applicable to the k-partite networks that maintain classification performance. The empirical analysis of hundreds of thousands of synthetically generated networks demonstrates the promise of coarsening techniques in solving large networks’ storage and processing problems. The findings indicate that the proposed coarsening algorithm achieved significant improvements in storage efficiency and classification runtime, even with modest reductions in the number of vertices, leading to over one-third savings in storage and twice faster classifications; furthermore, the classification performance metrics exhibited low variation on average. |
| first_indexed | 2024-03-09T01:20:38Z |
| format | Article |
| id | doaj.art-94799f75fe934f4abb5f515985438e1d |
| institution | Directory Open Access Journal |
| issn | 2364-8228 |
| language | English |
| last_indexed | 2024-03-09T01:20:38Z |
| publishDate | 2023-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj.art-94799f75fe934f4abb5f515985438e1d2023-12-10T12:11:01ZengSpringerOpenApplied Network Science2364-82282023-12-018112110.1007/s41109-023-00606-yCoarsening effects on k-partite network classificationPaulo Eduardo Althoff0Alan Demétrius Baria Valejo1Thiago de Paulo Faleiros2Department of Computer Science, University of BrasiliaDepartment of Computing, Federal University of São CarlosDepartment of Computer Science, University of BrasiliaAbstract The growing data size poses challenges for storage and computational processing time in semi-supervised models, making their practical application difficult; researchers have explored the use of reduced network versions as a potential solution. Real-world networks contain diverse types of vertices and edges, leading to using k-partite network representation. However, the existing methods primarily reduce uni-partite networks with a single type of vertex and edge. We develop a new coarsening method applicable to the k-partite networks that maintain classification performance. The empirical analysis of hundreds of thousands of synthetically generated networks demonstrates the promise of coarsening techniques in solving large networks’ storage and processing problems. The findings indicate that the proposed coarsening algorithm achieved significant improvements in storage efficiency and classification runtime, even with modest reductions in the number of vertices, leading to over one-third savings in storage and twice faster classifications; furthermore, the classification performance metrics exhibited low variation on average.https://doi.org/10.1007/s41109-023-00606-yNetwork semi-supervised learningNetwork coarseningHeterogeneous network |
| spellingShingle | Paulo Eduardo Althoff Alan Demétrius Baria Valejo Thiago de Paulo Faleiros Coarsening effects on k-partite network classification Applied Network Science Network semi-supervised learning Network coarsening Heterogeneous network |
| title | Coarsening effects on k-partite network classification |
| title_full | Coarsening effects on k-partite network classification |
| title_fullStr | Coarsening effects on k-partite network classification |
| title_full_unstemmed | Coarsening effects on k-partite network classification |
| title_short | Coarsening effects on k-partite network classification |
| title_sort | coarsening effects on k partite network classification |
| topic | Network semi-supervised learning Network coarsening Heterogeneous network |
| url | https://doi.org/10.1007/s41109-023-00606-y |
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