Explainability in Graph Neural Networks: A Taxonomic Survey

7 de January de 2021

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<meta name="Description" CONTENT="Artificial Intelligence Journal" />
<meta name="r0identifier" content="3d92323b5375746d21dcb172e8950adc" />
RxRegistration IDNº
R0Hash MD5 (of R3):3d92323b5375746d21dcb172e8950adc
R1Registration number (in the domain editorialia.com at WordPress):dmeditorialiawp.30108
R2Date-p-order (ddmmyyyypx): 07012021p1
R3Cid (combined id R1+R2):dmeditorialiawp.3010807012021p1
R4Resource official title:Explainability in Graph Neural Networks: A Taxonomic Survey
R5Publisher:arXiv.org
R6Resource website (1) ( #OpenAccess | #Openscience ): arxiv.org/abs/2012.15445
R9DOI:arXiv:2012.15445v1 [cs.LG]
R12Authors (separated by commas):Hao Yuan, Haiyang Yu, Shurui Gui, Shuiwang Ji
R14Keyword (selected 1 among the labels applied to this entry):=deeplearning
R15QR code (of the linked url at WP):
R16Time stamp URL:
R17Digital signature URL:Pending signature
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