Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate significantly from the majority within a graph. Over the years, extensive efforts in this field have been dedicated to the powerful capability of attrib...
Uložené v:
| Vydané v: | Applied sciences Ročník 15; číslo 15; s. 8691 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Basel
MDPI AG
01.08.2025
|
| Predmet: | |
| ISSN: | 2076-3417, 2076-3417 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate significantly from the majority within a graph. Over the years, extensive efforts in this field have been dedicated to the powerful capability of attributed networks to model real-world systems. Given the scarcity of labeled anomalies, current research primarily emphasizes model design via unsupervised learning. Graph autoencoders have been widely utilized for such purposes, leveraging the outstanding capabilities of Graph Neural Networks to model graph structured data. However, most existing graph autoencoder-based anomaly detectors do not exploit the nodes’ local subgraph information, limiting their ability to comprehensively understand the network for better representation learning. Moreover, these methods place greater emphasis on the attribute reconstruction process while neglecting the structure reconstruction aspect. This paper proposes an enhanced graph autoencoder framework for graph anomaly detection tasks that incorporates a subgraph extraction and aggregation preprocessing stage to utilize the nodes’ local topological information for enhanced embedding generation and to induce an additional node–subgraph view through model learning. A graph structure learning-based decoder is introduced as the structure decoder for better relationship learning. Finally, during the anomaly scoring stage, a node neighborhood selection technique is applied to enhance the detection performance. The effectiveness of the proposed framework is demonstrated through comprehensive experiments conducted on six commonly used real-world datasets. |
|---|---|
| AbstractList | Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate significantly from the majority within a graph. Over the years, extensive efforts in this field have been dedicated to the powerful capability of attributed networks to model real-world systems. Given the scarcity of labeled anomalies, current research primarily emphasizes model design via unsupervised learning. Graph autoencoders have been widely utilized for such purposes, leveraging the outstanding capabilities of Graph Neural Networks to model graph structured data. However, most existing graph autoencoder-based anomaly detectors do not exploit the nodes’ local subgraph information, limiting their ability to comprehensively understand the network for better representation learning. Moreover, these methods place greater emphasis on the attribute reconstruction process while neglecting the structure reconstruction aspect. This paper proposes an enhanced graph autoencoder framework for graph anomaly detection tasks that incorporates a subgraph extraction and aggregation preprocessing stage to utilize the nodes’ local topological information for enhanced embedding generation and to induce an additional node–subgraph view through model learning. A graph structure learning-based decoder is introduced as the structure decoder for better relationship learning. Finally, during the anomaly scoring stage, a node neighborhood selection technique is applied to enhance the detection performance. The effectiveness of the proposed framework is demonstrated through comprehensive experiments conducted on six commonly used real-world datasets. |
| Audience | Academic |
| Author | Zhang, Chi Jung, Jin-Woo |
| Author_xml | – sequence: 1 givenname: Chi surname: Zhang fullname: Zhang, Chi – sequence: 2 givenname: Jin-Woo surname: Jung fullname: Jung, Jin-Woo |
| BookMark | eNpNkU1PAyEQhonRxM-Tf2ATj6Z1gF2WPTZ-NjExRj2TWRjqNi2s7Pbgv5daNTIHyMvLkxneY7YfYiDGzjlMpWzgCvueV7zSquF77EhArSay5PX-v_MhOxuGJeTVcKk5HLHn2_COwZIr7hP278VsM0YKNjpKhY_pVw1xjavP4oZGsmMXQ_E2dGFRvGzaxbdhHrJ5jdurU3bgcTXQ2c9-wt7ubl-vHyaPT_fz69njxEolxwl512LpVCUIgWxJztWqbhorCAiEInDgRKta6UHwRvuyrVC7ttQSLJKTJ2y-47qIS9Onbo3p00TszLcQ08JgGju7IuNL6SVKVyllSy6wUd5VIIUArW3j6sy62LH6FD82NIxmGTcp5PaNFPlvBYcasmu6cy0wQ7s88pjQ5nK07mwOw3dZn-lKSKG03mIvdw9sisOQyP-1ycFsMzP_MpNfZsWKhQ |
| Cites_doi | 10.24963/ijcai.2017/299 10.24963/ijcai.2018/488 10.4028/www.scientific.net/AMM.320.226 10.1145/3437963.3441735 10.1145/3308558.3313488 10.1145/3442381.3449989 10.1145/3394486.3403062 10.20944/preprints202410.1354.v1 10.1145/3340531.3411903 10.2172/1592845 10.1145/3394486.3403118 10.1109/TNNLS.2021.3068344 10.1007/s00521-021-05924-9 10.1145/1401890.1402008 10.1007/978-3-031-05936-0_35 10.1145/3459637.3482057 10.1109/ICASSP40776.2020.9053387 10.1016/j.drudis.2021.02.011 10.1145/3488560.3498389 10.1007/s10115-006-0020-z 10.1145/3572403 10.1609/aaai.v34i01.5393 10.1137/1.9781611977653.ch79 10.1137/1.9781611975673.67 10.1109/DSAA53316.2021.9564233 10.1145/3488560.3498408 10.1109/ICCISci.2019.8716389 10.1162/089976601750264965 10.1145/3459637.3482195 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2025 MDPI AG 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2025 MDPI AG – notice: 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI DOA |
| DOI | 10.3390/app15158691 |
| DatabaseName | CrossRef ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central ProQuest One Community College ProQuest Central Korea Proquest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition DOAJ (Directory of Open Access Journals) |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest Central ProQuest One Academic Middle East (New) ProQuest One Academic UKI Edition ProQuest Central Essentials ProQuest Central Korea ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Sciences (General) |
| EISSN | 2076-3417 |
| ExternalDocumentID | oai_doaj_org_article_f43f3a3d566c412a96fd50322088c9d7 A852326887 10_3390_app15158691 |
| GroupedDBID | .4S 2XV 5VS 7XC 8CJ 8FE 8FG 8FH AADQD AAFWJ AAYXX ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ARCSS BCNDV BENPR CCPQU CITATION CZ9 D1I D1J D1K GROUPED_DOAJ IAO IGS ITC K6- K6V KC. KQ8 L6V LK5 LK8 M7R MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PROAC TUS ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI |
| ID | FETCH-LOGICAL-c363t-efdba4d652ea0ec4edd76799c2e0e026e0d0d2b6b3f02198f4b5a8db4830caed3 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001548983600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2076-3417 |
| IngestDate | Mon Nov 10 04:30:53 EST 2025 Wed Aug 13 11:40:47 EDT 2025 Tue Nov 04 18:10:56 EST 2025 Sat Nov 29 07:11:59 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 15 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c363t-efdba4d652ea0ec4edd76799c2e0e026e0d0d2b6b3f02198f4b5a8db4830caed3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://www.proquest.com/docview/3239021070?pq-origsite=%requestingapplication% |
| PQID | 3239021070 |
| PQPubID | 2032433 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_f43f3a3d566c412a96fd50322088c9d7 proquest_journals_3239021070 gale_infotracacademiconefile_A852326887 crossref_primary_10_3390_app15158691 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-08-01 |
| PublicationDateYYYYMMDD | 2025-08-01 |
| PublicationDate_xml | – month: 08 year: 2025 text: 2025-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Applied sciences |
| PublicationYear | 2025 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | ref_50 ref_14 Xiong (ref_3) 2021; 26 ref_13 ref_12 ref_11 ref_10 Ma (ref_17) 2013; 249 Liu (ref_26) 2021; 33 ref_18 ref_16 Zhang (ref_20) 2006; 10 ref_25 ref_24 ref_23 Sen (ref_45) 2008; 29 ref_22 ref_21 ref_29 ref_27 ref_36 ref_35 ref_32 ref_31 ref_30 Wang (ref_28) 2021; 33 Platt (ref_19) 2001; 13 ref_39 Yang (ref_15) 2023; 17 Deng (ref_33) 2021; 35 Chen (ref_34) 2020; 33 ref_47 Zhang (ref_37) 2021; 34 ref_46 ref_44 Alsentzer (ref_38) 2020; 33 ref_43 ref_42 ref_41 ref_40 ref_1 ref_2 ref_49 ref_48 ref_9 ref_8 ref_5 ref_4 ref_7 ref_6 |
| References_xml | – ident: ref_22 doi: 10.24963/ijcai.2017/299 – ident: ref_9 – ident: ref_21 doi: 10.24963/ijcai.2018/488 – ident: ref_5 – volume: 249 start-page: 226 year: 2013 ident: ref_17 article-title: Density-based distributed elliptical anomaly detection in wireless sensor networks publication-title: Appl. Mech. Mater. doi: 10.4028/www.scientific.net/AMM.320.226 – ident: ref_44 doi: 10.1145/3437963.3441735 – ident: ref_2 doi: 10.1145/3308558.3313488 – ident: ref_14 doi: 10.1145/3442381.3449989 – volume: 34 start-page: 15734 year: 2021 ident: ref_37 article-title: Nested graph neural networks publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_39 – ident: ref_48 doi: 10.1145/3394486.3403062 – ident: ref_42 – ident: ref_35 – volume: 29 start-page: 93 year: 2008 ident: ref_45 article-title: Collective classification in network data publication-title: AI Mag. – ident: ref_8 – ident: ref_40 doi: 10.20944/preprints202410.1354.v1 – ident: ref_12 doi: 10.1145/3340531.3411903 – ident: ref_31 doi: 10.2172/1592845 – volume: 35 start-page: 4027 year: 2021 ident: ref_33 article-title: Graph neural network-based anomaly detection in multivariate time series publication-title: Proc. AAAI Conf. Artif. Intell. – ident: ref_4 doi: 10.1145/3394486.3403118 – volume: 33 start-page: 2378 year: 2021 ident: ref_26 article-title: Anomaly detection on attributed networks via contrastive self-supervised learning publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2021.3068344 – volume: 33 start-page: 12073 year: 2021 ident: ref_28 article-title: One-class graph neural networks for anomaly detection in attributed networks publication-title: Neural Comput. Appl. doi: 10.1007/s00521-021-05924-9 – ident: ref_47 doi: 10.1145/1401890.1402008 – ident: ref_10 – ident: ref_49 doi: 10.1007/978-3-031-05936-0_35 – ident: ref_41 – ident: ref_27 doi: 10.1145/3459637.3482057 – ident: ref_25 doi: 10.1109/ICASSP40776.2020.9053387 – volume: 26 start-page: 1382 year: 2021 ident: ref_3 article-title: Graph neural networks for automated de novo drug design publication-title: Drug Discov. Today doi: 10.1016/j.drudis.2021.02.011 – ident: ref_1 doi: 10.1145/3488560.3498389 – volume: 10 start-page: 333 year: 2006 ident: ref_20 article-title: Detecting outlying subspaces for high-dimensional data: The new task, algorithms, and performance publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-006-0020-z – ident: ref_7 – ident: ref_30 – volume: 17 start-page: 1 year: 2023 ident: ref_15 article-title: RoSGAS: Adaptive social bot detection with reinforced self-supervised GNN architecture search publication-title: ACM Trans. Web doi: 10.1145/3572403 – ident: ref_11 – ident: ref_13 doi: 10.1609/aaai.v34i01.5393 – ident: ref_36 doi: 10.1137/1.9781611977653.ch79 – ident: ref_18 – ident: ref_24 doi: 10.1137/1.9781611975673.67 – ident: ref_23 doi: 10.1109/DSAA53316.2021.9564233 – volume: 33 start-page: 19314 year: 2020 ident: ref_34 article-title: Iterative deep graph learning for graph neural networks: Better and robust node embeddings publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_6 – ident: ref_32 doi: 10.1145/3488560.3498408 – ident: ref_50 – ident: ref_46 – volume: 33 start-page: 8017 year: 2020 ident: ref_38 article-title: Subgraph neural networks publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_16 doi: 10.1109/ICCISci.2019.8716389 – volume: 13 start-page: 1443 year: 2001 ident: ref_19 article-title: Estimating the support of a high-dimensional distribution publication-title: Neural Comput. doi: 10.1162/089976601750264965 – ident: ref_43 – ident: ref_29 doi: 10.1145/3459637.3482195 |
| SSID | ssj0000913810 |
| Score | 2.3262482 |
| Snippet | Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate... |
| SourceID | doaj proquest gale crossref |
| SourceType | Open Website Aggregation Database Index Database |
| StartPage | 8691 |
| SubjectTerms | Analysis Datasets Deep learning Design Detectors Euclidean space graph anomaly detection graph autoencoders graph neural networks graph structure learning Neighborhoods Neural networks Social network analysis Social networks |
| SummonAdditionalLinks | – databaseName: DOAJ (Directory of Open Access Journals) dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEB5KyKE5lDzpNg90CKQ5mMiWVraOm2S3PYUWWshNjF4kkHrLrhPIv89I9obtIeTSq23MMKN5fGjmG4BTxMhFFK4oYySAIrQsEAMvrHXKN94JFJiXTdQ3N83trf6xtuor9YT19MC94i6ipF-h8FR2OFlWqFX0Y07HkNzDaZ_nyHmt18BUjsG6TNRV_UCeIFyf7oNT7m6ULv9JQZmp_614nJPMbBs-DdUhm_RS7cCH0O7C1hpn4C7sDN64ZF8HyujzPfg5be_yVT77lgio2eSxmyeGSh8WjKrS1dN2_gcfntl16HIDVstywwCj4JF5q9kwm5Re7cPv2fTX1fdiWJZQOKFEV4ToLUqvxlVAHpwM3teq1tpVgQcCWoF77iurrIiU1nUTpR1j461sBHcYvDiAjXbehs_AUJKfYioU4lgqm4a4VUWGK5u6ttzWIzhd6c_87TkxDGGJpGazpuYRXCbdvn6SiKzzAzKvGcxr3jPvCM6SZUxyt26BDoepAZI0EVeZSUNIulIUKkdwtDKeGfxwaURFQhGqrfmX_yHNIXys0v7f3AB4BBvd4jEcw6Z76u6Xi5N8BF8A9UTgRA priority: 102 providerName: Directory of Open Access Journals |
| Title | Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information |
| URI | https://www.proquest.com/docview/3239021070 https://doaj.org/article/f43f3a3d566c412a96fd50322088c9d7 |
| Volume | 15 |
| WOSCitedRecordID | wos001548983600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZKywEO0BZQF0rlQyXgEOHEXsc5VVvYthxYLQikcrL8LEg0KdkUiX_fGa-3lAO99Bg7ykPz8Hz2zDeE7BsTGY_cFWWMAFB4IwpjAiusddIr77jhJjWbqGczdXrazPOG2yKnVa58YnLUvnO4R_6WV4DOAZ_U7ODiV4Fdo_B0NbfQuEc2kKkM9HzjcDqbf77eZUHWS1WyZWEehyfguTCu4Uo25T9LUWLs_59fTovN0eO7fuYmeZTDTDpZ6sUWWQvtNnl4g3xwm2xls17Q15l7-s0T8mnafk85AfQYmazp5HLokOrSh55CeLsabbtz8_MPfR-GlMnV0pR5QMELJQJsmouccOop-Xo0_fLupMhdFwrHJR-KEL01wstxFQwLTgTva1k3jasCC4DYAvPMV1ZaHuFHGxWFHRvlrVCcORM8f0bW264NO4QaAQZvMOKIYyEtVoPLCjSgVHVtma1HZH8lAH2xJNfQAEpQTvqGnEbkEIVzfQsyYqeBrj_T2cB0FKByhnsIT50oK9PI6McM3BW4Udd4eNcrFK1Gux1640wuP4AvRQYsPVEAySsJPndEdlei1dmgF_qvXJ_fPv2CPKiwRXDKEdwl60N_GV6S--738GPR72X93EvQH67mHz7Ov10BRbLyvQ |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwEB2VLRJwAFpALBTwoQg4RDix4yQHhBba0lXb1SIVqZyMPwEJkpJNQf1T_EbG3qSUA9x64OpEiZO8vJmxZ94AbCrlKfPMJKn3GKCwiidKOZpobYQtrWGKqdhsopjNyqOjar4CP4damJBWOXBiJGrbmLBG_pxlGJ1jfFLQl8ffktA1KuyuDi00lrDYc6c_MGRbvJhu4fd9nGU724evd5O-q0BimGBd4rzViluRZ05RZ7izthBFVZnMUYcRiaOW2kwLzTzeryo917kqreYlo0Y5y_C6l2CVB7CPYHU-PZi_P1vVCSqbZUqXhYAMZxz2oYPPUIoq_cP0xQ4Bf7MD0bjt3PjfXstNuN670WSyxP0arLh6Ha6dE1dch7Wethbkaa-t_ewWvN2uP8WcB_ImKHWTyUnXBClP61qC7vswWjdf1ZdTsuW6mKlWk5hZQZBlo8A36Yu4wqHb8O5CHvQOjOqmdneBKI6EpoJH5XMudKh2FxkiPC2LQlNdjGFz-ODyeCkeIjHoCriQ53AxhlcBDGenBMXvONC0H2VPINJz_KUUs-h-G55mqhLe5hTpGM2EqSze60mAkgy81LXKqL68AmcaFL7kpMzReRZoU8awMUBJ9oS1kL9xdO_fhx_Bld3Dg325P53t3YerWWiHHPMhN2DUtSfuAVw237vPi_Zh_28Q-HDRuPsFiBZQtA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwEB2VLUJwAFpALBTwoQg4RHVs5-uA0MJ2YVVYLRJI7cn4s0WCbMmmoP41fh1jb1LKAW49cE2iOImf38w4M28AtpXylHtuktR7DFB4JRKlHE20NrktreGKq9hsopjNyv39ar4GP_tamJBW2XNiJGq7MGGPfIczjM4xPinoju_SIubjyYvjb0noIBX-tPbtNFYQ2XOnPzB8Wz6fjnGuHzM22f3w6k3SdRhIDM95mzhvtRI2z5hT1BnhrC3yoqoMc9RhdOKopZbpXHOPY1elFzpTpdWi5NQoZzne9xKso0su2ADW59N384OzHZ6guFmmdFUUyPHpwz_p4D-UeZX-YQZjt4C_2YRo6CY3_udPdBOud-41Ga3WwwasuXoTrp0TXdyEjY7OluRpp7n97Ba8362PYi4EeR0UvMnopF0EiU_rGoJufX-0XnxVX07J2LUxg60mMeOCIPtG4W_SFXeFU7fh44W86B0Y1Iva3QWiBBKdCp6Wz0SuQxV8zhD5aVkUmupiCNv95MvjlaiIxGAsYESew8gQXgZgnF0SlMDjgUVzKDtikV7gUlPcoltuRMpUlXubUaRpNB-msjjWkwArGfiqbZRRXdkFPmlQ_pKjMkOnOkdbM4StHlayI7Kl_I2pe_8-_QiuINjk2-ls7z5cZaFLckyT3IJB25y4B3DZfG8_L5uH3TIh8OmiYfcLDOdZdA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Enhanced+Graph+Autoencoder+for+Graph+Anomaly+Detection+Using+Subgraph+Information&rft.jtitle=Applied+sciences&rft.au=Zhang%2C+Chi&rft.au=Jung%2C+Jin-Woo&rft.date=2025-08-01&rft.issn=2076-3417&rft.eissn=2076-3417&rft.volume=15&rft.issue=15&rft.spage=8691&rft_id=info:doi/10.3390%2Fapp15158691&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_app15158691 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon |