Normalized total loss on the Facebook dataset (DCOR with and without RLC).
<p>To enable a fair visual comparison of training dynamics, each curve is normalized as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0335135#pone.0335135.e243" target="_blank">Eq (29)</a> by dividing by its epoch-1 value and EMA-smoot...
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| _version_ | 1849927640793743360 |
|---|---|
| author | Hossein Rafieizadeh (22676722) |
| author2 | Hadi Zare (20073000) Mohsen Ghassemi Parsa (22676725) Hocine Cherifi (8177628) |
| author2_role | author author author |
| author_facet | Hossein Rafieizadeh (22676722) Hadi Zare (20073000) Mohsen Ghassemi Parsa (22676725) Hocine Cherifi (8177628) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hossein Rafieizadeh (22676722) Hadi Zare (20073000) Mohsen Ghassemi Parsa (22676725) Hocine Cherifi (8177628) |
| dc.date.none.fl_str_mv | 2025-11-24T18:37:58Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0335135.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Normalized_total_loss_on_the_Facebook_dataset_DCOR_with_and_without_RLC_/30698017 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified intrusions across social reconstructions across views level contrastive learning dual contrastive learning across six benchmarks div >< p view discrepancies underutilized augmented graph views dcor improves auroc view discrepancies level contrast dual autoencoder augmented view specific information six datasets reduces auroc publicly available preserves fine physical domains performing non maximum gain leaving cross identifying threats financial fraud existing graph dcor reconstructs dcor ), contrasts reconstructions attributed networks attribute patterns |
| dc.title.none.fl_str_mv | Normalized total loss on the Facebook dataset (DCOR with and without RLC). |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>To enable a fair visual comparison of training dynamics, each curve is normalized as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0335135#pone.0335135.e243" target="_blank">Eq (29)</a> by dividing by its epoch-1 value and EMA-smoothed (exponential moving average) with . The EMA is computed as with . This normalization emphasizes relative convergence behavior (shape and stability) rather than raw magnitudes: with RLC, the total objective continues to decrease in late epochs, whereas without RLC it plateaus, consistent with the ablation trends in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0335135#pone.0335135.t005" target="_blank">Table 5</a>.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_ac00d616de33d0d53c63092392b46c74 |
| identifier_str_mv | 10.1371/journal.pone.0335135.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30698017 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Normalized total loss on the Facebook dataset (DCOR with and without RLC).Hossein Rafieizadeh (22676722)Hadi Zare (20073000)Mohsen Ghassemi Parsa (22676725)Hocine Cherifi (8177628)Cell BiologyScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedintrusions across socialreconstructions across viewslevel contrastive learningdual contrastive learningacross six benchmarksdiv >< pview discrepancies underutilizedaugmented graph viewsdcor improves aurocview discrepancieslevel contrastdual autoencoderaugmented viewspecific informationsix datasetsreduces aurocpublicly availablepreserves finephysical domainsperforming nonmaximum gainleaving crossidentifying threatsfinancial fraudexisting graphdcor reconstructsdcor ),contrasts reconstructionsattributed networksattribute patterns<p>To enable a fair visual comparison of training dynamics, each curve is normalized as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0335135#pone.0335135.e243" target="_blank">Eq (29)</a> by dividing by its epoch-1 value and EMA-smoothed (exponential moving average) with . The EMA is computed as with . This normalization emphasizes relative convergence behavior (shape and stability) rather than raw magnitudes: with RLC, the total objective continues to decrease in late epochs, whereas without RLC it plateaus, consistent with the ablation trends in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0335135#pone.0335135.t005" target="_blank">Table 5</a>.</p>2025-11-24T18:37:58ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0335135.g006https://figshare.com/articles/figure/Normalized_total_loss_on_the_Facebook_dataset_DCOR_with_and_without_RLC_/30698017CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306980172025-11-24T18:37:58Z |
| spellingShingle | Normalized total loss on the Facebook dataset (DCOR with and without RLC). Hossein Rafieizadeh (22676722) Cell Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified intrusions across social reconstructions across views level contrastive learning dual contrastive learning across six benchmarks div >< p view discrepancies underutilized augmented graph views dcor improves auroc view discrepancies level contrast dual autoencoder augmented view specific information six datasets reduces auroc publicly available preserves fine physical domains performing non maximum gain leaving cross identifying threats financial fraud existing graph dcor reconstructs dcor ), contrasts reconstructions attributed networks attribute patterns |
| status_str | publishedVersion |
| title | Normalized total loss on the Facebook dataset (DCOR with and without RLC). |
| title_full | Normalized total loss on the Facebook dataset (DCOR with and without RLC). |
| title_fullStr | Normalized total loss on the Facebook dataset (DCOR with and without RLC). |
| title_full_unstemmed | Normalized total loss on the Facebook dataset (DCOR with and without RLC). |
| title_short | Normalized total loss on the Facebook dataset (DCOR with and without RLC). |
| title_sort | Normalized total loss on the Facebook dataset (DCOR with and without RLC). |
| topic | Cell Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified intrusions across social reconstructions across views level contrastive learning dual contrastive learning across six benchmarks div >< p view discrepancies underutilized augmented graph views dcor improves auroc view discrepancies level contrast dual autoencoder augmented view specific information six datasets reduces auroc publicly available preserves fine physical domains performing non maximum gain leaving cross identifying threats financial fraud existing graph dcor reconstructs dcor ), contrasts reconstructions attributed networks attribute patterns |