Shushanya imiyoboro ya Neural
Imiyoboro ishushanya (GNNs) nicyitegererezo cyiga muburyo butaziguye amakuru yashushanyije - imitwe ihujwe nimpande - mukunyuza no gukusanya amakuru hagati yabaturanyi.
Incamake
They matter because much of the real world is relational: social networks, molecules, road maps, and recommendation systems are all graphs that grids and sequences cannot naturally represent.
Kwibira cyane
GNN ikora binyuze mubutumwa butambuka. Buri pfundo ritangirana nibiranga vector, kandi muri buri cyiciro buri node ikusanya ubutumwa buva mubaturanyi bayo, ikabateranya hamwe na permis-invariant imikorere nkumubare, bivuze, cyangwa max, kandi ikavugurura ibyayo. Gutondekanya L bireka amakuru akwirakwiza L hops ku gishushanyo, bityo umusozo wanyuma ugaragaza umuturanyi mugari, ntabwo bihuza gusa. Ibihinduka bitandukanye muburyo byegeranya: Graph Convolutional Networks ikoresha umuturanyi usanzwe ugereranije, GraphSAGE ntangarugero kandi igateranya umubare uteganijwe wabaturanyi kugirango bapime, kandi Graph Attention Networks yiga uburemere kuburyo node yitabira cyane kubaturanyi bakomeye. Kwiga node, inkombe, cyangwa igishushanyo-cyuzuye cyo gushushanya noneho kugaburira ibyiciro, gusubira inyuma, cyangwa guhuza-guhanura imitwe.
Ubushishozi
Ibisobanuro byumutungo ni permis invariance: igishushanyo ntigishobora gutondekanya node, bityo intambwe yo guteranya igomba gutanga umusaruro umwe utitaye kuburyo abaturanyi bashyizwe kurutonde - niyo mpamvu umubare, bivuze, cyangwa max aho kuba ibikorwa bihamye. Intambamyi izwi ni ukurenza urugero: shyira ubutumwa bwinshi-butambutsa ubutumwa kandi buri kintu cyinjiza gihuza agaciro kamwe, kwoza itandukaniro ryingirakamaro. Ibi bifata ubujyakuzimu bufatika kandi butera guhuza ibisigisigi nibisanzwe.
Ingaruka z'Ingamba
Ibyemezo bisobanutse
Iragufasha gutandukanya ibyifuzo bya tekiniki bisobanutse nururimi rwo kwamamaza.
Igiciro na bije
Urashobora kubaza ibibazo byiza byo gushyira mubikorwa mbere yo gukoresha amafaranga cyangwa igihe.
Itsinda hamwe nakazi
Amakipe asangiye ibitekerezo akora ibicuruzwa byiza, politiki, nibyemezo byo kwiga.
Kazoza ka Graph Neural Networks
GNNs ningenzi muri siyansi yubumenyi. GNoME ya DeepMind yabakoresheje mu guhanura amamiriyoni mashya yuburyo bushya bwa kirisiti, kandi imiterere yikirere nka GraphCast igereranya isi nkigishushanyo cyo guhanura byihuse kuruta abigana fiziki. Ubushakashatsi burimo guhangana nubunini bugera kuri miliyari-shusho, imiyoboro yimbitse irwanya koroshya cyane, nubusabane hagati ya GNN na Transformers (byibanze cyane kubishushanyo bihujwe). Tegereza kwishyira hamwe hamwe nicyitegererezo cyibanze no gukoresha ikoreshwa mubuvumbuzi bwibiyobyabwenge nibikoresho bya siyansi.
Gushyira mu bikorwa Isi
Guteganya imiterere ya molekuline nuburozi mubuvumbuzi bwibiyobyabwenge ukoresheje atome nkumutwe nububiko bwimiti nkimpande.
Guha imbaraga ibyifuzo kumasosiyete nka Pinterest, aho PinSage yiga gushira hejuru yishusho yibintu no guhuza abakoresha.
Kumenya uburiganya no kunyereza amafaranga mugushakisha uburyo buteye inkeke mubishushanyo mbonera hagati ya konti.
Iteganyagihe ikirere n’umuhanda, nko muri GraphCast na moderi-yumuhanda werekana aho uhurira.
Ingaruka & Kurinda
Amakipe atandukanye arashobora gukoresha ijambo rimwe muburyo butandukanye, sobanura intera hakiri kare.
Ibipimo birashobora kugaragara bikomeye mugihe imikorere-yisi-itaringaniye.
Kwirengagiza ubuziranenge bwamakuru na gahunda yo gusuzuma akenshi bitanga ibisubizo byoroshye.
Igishushanyo mbonera
Tangira nururimi rusobanutse rwibisubizo ukeneye.
Toranya intsinzi imwe hamwe nuburyo bumwe bwo gutsindwa mbere yo kwipimisha.
Koresha umuderevu muto hamwe namakuru ahagarariye, ntabwo ari demo yashizweho.
Inyandiko aho Graph Neural Networks ifasha nuburyo bworoshye bworoshye.
Komeza Ubushakashatsi
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Ubuyobozi bukurikira
Imiyoboro ya Neural
Ibibazo bikunze kubazwa
What is Graph Neural Networks?
Imiyoboro ishushanya (GNNs) nicyitegererezo cyiga muburyo butaziguye amakuru yashushanyije - imitwe ihujwe nimpande - mukunyuza no gukusanya amakuru hagati yabaturanyi. Bifite akamaro kuko ibyinshi mubyukuri bifatika bifitanye isano: imbuga nkoranyambaga, molekile, amakarita yumuhanda, hamwe na sisitemu yo gutanga ibyifuzo byose ni ibishushanyo mbonera hamwe na gahunda bidashobora guhagararirwa bisanzwe.
Nubuhe buryo bwibanze bwo kubara bwibishushanyo mbonera byinshi?
GNNs ikora kugirango buri node ikusanyirize hamwe kandi ikusanyirize hamwe ubutumwa buturutse mubaturanyi bayo, hanyuma ivugurure ubwayo, isubirwamo mubice.
Ni ukubera iki ibikorwa bya GNN bituranye-gukusanya ibikorwa bigomba guhinduka?
Kubera ko nta tegeko ryemewe ryabaturanyi ba node, ibikorwa nkamafaranga, bivuze, cyangwa max byemeza ko umusaruro udaterwa nuburyo abaturanyi bashyizwe kurutonde.
Niki 'kurenza urugero' bivuga iki muri GNN zimbitse?
Gutondekanya ubutumwa bwinshi-butambutsa ibice bituma buri node igereranya guhuza kugiciro kimwe, guhanagura itandukaniro ryingirakamaro.
Niki gitandukanya Urusobekerane rwibishushanyo (GAT) nurufatiro rwibanze rwa Graph Convolutional Network (GCN)?
GATs igenera uburemere bwitondewe kubaturanyi, kureka urwego rukibanda kubyingenzi aho kubigereranya kimwe.
Muri GNN ikoreshwa kuri molekile, ubusanzwe impande zombi zerekana iki?
Molekile ni ibishushanyo bisanzwe: atome ni node kandi imihuza ibahuza ni impande, niyo mpamvu GNNs irusha abandi guhanura imiterere ya molekile.