Encoder embedding for general graph and node classification
Abstract Graph encoder embedding, a recent technique for graph data, offers speed and scalability in producing vertex-level representations from binary graphs. In this paper, we extend the applicability of this method to a general graph model, which includes weighted graphs, distance matrices, and k...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-10-01
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| Series: | Applied Network Science |
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| Online Access: | https://doi.org/10.1007/s41109-024-00678-4 |
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| _version_ | 1850203795773980672 |
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| author | Cencheng Shen |
| author_facet | Cencheng Shen |
| author_sort | Cencheng Shen |
| collection | DOAJ |
| description | Abstract Graph encoder embedding, a recent technique for graph data, offers speed and scalability in producing vertex-level representations from binary graphs. In this paper, we extend the applicability of this method to a general graph model, which includes weighted graphs, distance matrices, and kernel matrices. We prove that the encoder embedding satisfies the law of large numbers and the central limit theorem on a per-observation basis. Under certain condition, it achieves asymptotic normality on a per-class basis, enabling optimal classification through discriminant analysis. These theoretical findings are validated through a series of experiments involving weighted graphs, as well as text and image data transformed into general graph representations using appropriate distance metrics. |
| format | Article |
| id | doaj-art-33afcf04b0804afd8f9fa9ed8ee3f50b |
| institution | OA Journals |
| issn | 2364-8228 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj-art-33afcf04b0804afd8f9fa9ed8ee3f50b2025-08-20T02:11:25ZengSpringerOpenApplied Network Science2364-82282024-10-019111710.1007/s41109-024-00678-4Encoder embedding for general graph and node classificationCencheng Shen0Department of Applied Economics and Statistics, University of DelawareAbstract Graph encoder embedding, a recent technique for graph data, offers speed and scalability in producing vertex-level representations from binary graphs. In this paper, we extend the applicability of this method to a general graph model, which includes weighted graphs, distance matrices, and kernel matrices. We prove that the encoder embedding satisfies the law of large numbers and the central limit theorem on a per-observation basis. Under certain condition, it achieves asymptotic normality on a per-class basis, enabling optimal classification through discriminant analysis. These theoretical findings are validated through a series of experiments involving weighted graphs, as well as text and image data transformed into general graph representations using appropriate distance metrics.https://doi.org/10.1007/s41109-024-00678-4Graph embeddingGeneral graphAsymptotic theory |
| spellingShingle | Cencheng Shen Encoder embedding for general graph and node classification Applied Network Science Graph embedding General graph Asymptotic theory |
| title | Encoder embedding for general graph and node classification |
| title_full | Encoder embedding for general graph and node classification |
| title_fullStr | Encoder embedding for general graph and node classification |
| title_full_unstemmed | Encoder embedding for general graph and node classification |
| title_short | Encoder embedding for general graph and node classification |
| title_sort | encoder embedding for general graph and node classification |
| topic | Graph embedding General graph Asymptotic theory |
| url | https://doi.org/10.1007/s41109-024-00678-4 |
| work_keys_str_mv | AT cenchengshen encoderembeddingforgeneralgraphandnodeclassification |