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Grace: a scalable framework for graph embedding with autoencoder-based feature compression and random walks
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| Published in: | JDSA |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1864030192251437060 |
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| collection | WordPress RSS FRELIP Feed Integration |
| container_title | JDSA |
| description | |
| discipline_display | Technology & Engineering |
| discipline_facet | Technology & Engineering |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:13958 |
| institution | FRELIP |
| journal_source_facet | JDSA |
| publishDate | 2026 |
| publishDateSort | 2026 |
| record_format | rss_article |
| spellingShingle | Grace: a scalable framework for graph embedding with autoencoder-based feature compression and random walks Big data and data science Technology & Engineering — Computing Technology & Engineering |
| sub_discipline_display | Technology & Engineering — Computing |
| sub_discipline_facet | Technology & Engineering — Computing |
| subject_display | Big data and data science Technology & Engineering — Computing Technology & Engineering Big data and data science Technology & Engineering — Computing Technology & Engineering |
| subject_facet | Big data and data science Technology & Engineering — Computing Technology & Engineering |
| title | Grace: a scalable framework for graph embedding with autoencoder-based feature compression and random walks |
| title_auth | Grace: a scalable framework for graph embedding with autoencoder-based feature compression and random walks |
| title_full | Grace: a scalable framework for graph embedding with autoencoder-based feature compression and random walks |
| title_fullStr | Grace: a scalable framework for graph embedding with autoencoder-based feature compression and random walks |
| title_full_unstemmed | Grace: a scalable framework for graph embedding with autoencoder-based feature compression and random walks |
| title_short | Grace: a scalable framework for graph embedding with autoencoder-based feature compression and random walks |
| title_sort | grace: a scalable framework for graph embedding with autoencoder-based feature compression and random walks |
| topic | Big data and data science Technology & Engineering — Computing Technology & Engineering |
| url | https://link.springer.com/article/10.1007/s41060-026-01091-z |