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Graph Neural Networks概観

Graph Neural Networks概観

Graph Neural Networks (GNNs)についてまとめました
実際にどんなことに使えるかイメージを持ってもらうことを意識しました。
内容は以下の通りです。

・グラフ構造とは
・GNNsの発展
・いくつかのGNNsの特徴
・NLPにおける利用例
・その他の領域における利用例

勉強会好評でやってよかったです!
フィードバックはTwitter (roy29fuku)にお願いします。

追記:
なるべくリンク埋め込んで簡単に参照できるようにしたので、ダウンロードしておつかいただくと便利です

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Ryota Yamada

June 01, 2019
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  1. # ਪ঑ϓϩτίϧ 1. 8िྸΦεͷϚ΢εAΛ12ඖ༻ҙ 2. 5×105ͷࡉ๔BΛi.v. 3-1. ౤༩܈6ඖʹༀࡎYΛ10μg/kg i.p. 3-2.

    ରর܈6ඖʹsalineΛi.p. # ධՁ ମԹɾ݂ѹ ࣬ױXʹༀࡎY͕ ޮ͔͘ௐ΂͍ͨ ࣄۀ֓ཁ 3 3 ৽͍͠ݚڀΞΠσΟΞʹରͯ͠ɺ աڈͷ࿦จ͔Βऔಘ࣮ͨ͠ݧ৚݅Λ΋ͱʹ࣮ݧϓϩτίϧΛఏҊ ௐࠪ࣌ؒ΍࣮ݧͷ΍Γ௚͠Λ࡟ݮ
  2. ख๏ 4 4 named entity recognition, relation extraction, coreference resolutionΛ༻͍ͯ

    ࿦จ͔Βϓϩτίϧ΍݁Ռʹؔ͢ΔσʔλΛநग़ɺߏ଄Խ σʔλΛάϥϑߏ଄ʹஔ͖׵͑Δ͜ͱͰϓϩτίϧͷൺֱ΍ੜ੒͕Ͱ͖Δ
  3. ख๏ 5 5 named entity recognition, relation extraction, coreference resolutionΛ༻͍ͯ

    ࿦จ͔Βϓϩτίϧ΍݁Ռʹؔ͢ΔσʔλΛநग़ɺߏ଄Խ σʔλΛάϥϑߏ଄ʹஔ͖׵͑Δ͜ͱͰϓϩτίϧͷൺֱ΍ੜ੒͕Ͱ͖Δ ɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹ͔Ͳ͏͔֬ೝ͢ΔͨΊʹGNNsษڧ࢝Ί·ͨ͠
  4. ख๏ 6 https://speakerdeck.com/ioki/development-of-mrr-at-cookpadtechconf2019 named entity recognition, relation extraction, coreference resolutionΛ༻͍ͯ

    ࿦จ͔Βϓϩτίϧ΍݁Ռʹؔ͢ΔσʔλΛநग़ɺߏ଄Խ σʔλΛάϥϑߏ଄ʹஔ͖׵͑Δ͜ͱͰϓϩτίϧͷൺֱ΍ੜ੒͕Ͱ͖Δ ɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹ͔Ͳ͏͔֬ೝ͢ΔͨΊʹGNNsษڧ࢝Ί·ͨ͠ Luan et al., 2018 # ઌߦݚڀ Պֶ࿦จ͔Βknowledge graph ϨγϐΛػցՄಡʹ
  5. ख๏ 7 named entity recognition, relation extraction, coreference resolutionΛ༻͍ͯ ࿦จ͔Βϓϩτίϧ΍݁Ռʹؔ͢ΔσʔλΛநग़ɺߏ଄Խ

    σʔλΛάϥϑߏ଄ʹஔ͖׵͑Δ͜ͱͰϓϩτίϧͷൺֱ΍ੜ੒͕Ͱ͖Δ ɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹ͔Ͳ͏͔֬ೝ͢ΔͨΊʹGNNsษڧ࢝Ί·ͨ͠ Luan et al., 2018 # ઌߦݚڀ Պֶ࿦จ͔Βknowledge graph ϨγϐΛػցՄಡʹ ϝϯόʔืूத ڵຯ͋Δํ ࿈བྷ͍ͩ͘͞ʂ Twitter: @roy29fuku https://speakerdeck.com/ioki/development-of-mrr-at-cookpadtechconf2019
  6. Graphͱ͸ 12 12 ӈͷάϥϑ͸͜Μͳײ͡ʹදݱ ɹ௖఺ू߹V = {1, 2, 3, 4}

    ɹลू߹E = {{1, 2}, {1, 3}, {2, 3}, {2, 4}, {3, 4}} ɹάϥϑG = (V, E) ผͷݴ͍ํͰఆٛ͢Δͱ ɹE ⊆ [V]2 Λຬͨ͢ ɹG = (V, E) ͷू߹ͷ૊ ΛάϥϑͱݺͿ 1 2 3 4
  7. GraphͰදݱͰ͖Δ΋ͷ 14 14 ϊʔυ: ਓʢஉੑ, ঁੑ, …ʣ Τοδ: ਓؒؔ܎ʢ༑ਓ, …ʣ

    Structured deep models: Deep learning on graphs and beyond karate club
  8. GraphͰදݱͰ͖Δ΋ͷ 15 15 ϊʔυ: ݪࢠʢC, H, …ʣ Τοδ: ݁߹ʢ୯݁߹, …ʣ

    Structured deep models: Deep learning on graphs and beyond
  9. ne[n]: ϊʔυnͱྡ઀͢Δϊʔυू߹ ln: ϊʔυnͷϥϕϧ xn: ϊʔυnͷঢ়ଶ on: ϊʔυnͷग़ྗ ·ͱΊΔͱ Graph

    Neueral Networks [Gori+ 2005] 25 25 Fw͕contraction mappingʢॖईࣸ૾ʣͳΒ Fw, Gw͸ղΛ࣋ͪɺunique
  10. GNNsͷྺ࢙ᶄ Convolution Structured deep models: Deep learning on graphs and

    beyond CNNͷ৞ΈࠐΈͷ֓೦Λಋೖͯ͠ܭࢉޮ཰↑ 26 26
  11. Graph Convolutional Netoworks [Duvenaud+ 2015, Li+ 2016, Scichtkrull+ 2017] 28

    28 CNNͷ৞ΈࠐΈͷ֓೦ΛGraphʹద༻ άϥϑߏ଄͸ඇఆܕʢϊʔυͷ਺ɺ֤ϊʔυͷྡ઀ϊʔυ਺ʣ →৞ΈࠐΈͷૢ࡞Λద༻͢ΔͨΊʹ޻෉͕ඞཁ Wu et al., 2019 Euclidean non-Euclidean
  12. Graph Convolutional Netoworks [Duvenaud+ 2015, Li+ 2016, Scichtkrull+ 2017] 29

    29 CNNͷ৞ΈࠐΈͷ֓೦ΛGraphʹద༻ ΤοδΛॏΈͱͯ͠ѻͬͨ Structured deep models: Deep learning on graphs and beyond
  13. Relational Graph Convolutional Netoworks [Scichtkrull+ 2017] 30 30 relationཁૉΛ௥Ճ graph:

    G = (V, E, R) nodes: vi ∈ V edges: (vi, r, vj) ∈ E relation type: r ∈ R Schlichtkrull et al., 2017
  14. Relational Graph Convolutional Netoworks [Scichtkrull+ 2017] 31 31 hi(l): l૚໨ͷϊʔυvi

    ͷhidden state Ni r: ϊʔυvi ʹରͯؔ͠܎rͰྡ઀͢Δϊʔυू߹ ci,r: ྡ઀͢Δϊʔυͷ਺Λਖ਼نԽɺex. |Nir| ͜ΕΛ֤ϊʔυ͝ͱʹฒྻͰܭࢉɺ࣮ફతʹ͸ܭ ࢉͷޮ཰ͷͨΊʹsparse matrixΛ༻͍Δ ૚ΛॏͶΔ͜ͱͰɺෳ਺ͷϦϨʔγϣϯΛ·͙ͨ ϊʔυಉ࢜ͷؔ܎Λಋ͘ͷʹ໾ཱͭ ͋Δϊʔυʢ੺ʣʹண໨ͨ࣌͠ͷߋ৽ ֤ؔ܎ੑͷೖग़ྗʹؔΘΔྡ઀ϊʔυɾࣗݾϧʔ ϓ΋ܭࢉͯͦ͠ΕͧΕ྘ͷߦྻΛऔಘɺ concatɺReLUͰ෼ྨ Schlichtkrull et al., 2017
  15. Graph Attention Networks [Monti+ 2017, Hoshen 2017, Veličković+ 2018] 34

    34 Ͳͷϊʔυͱͷྡ઀Λॏࢹ͢Δ͔attentionͰදݱͨ͠ GCNͰaij(1/cij)͸ྡ઀ϊʔυ਺ʹґଘ͍ͯͨ͠ Graph Attention NetworksͰ͸ΑΓॏཁͳ΋ͷΛॏΈ෇͚Δ Wu et al., 2019 GCN Graph Attention Networks
  16. GNNsͷྺ࢙ᶆ άϥϑͷੜ੒ Structured deep models: Deep learning on graphs and

    beyond Version 1: Generate graph (or predict new links) between known entities Version 2: Generate graphs from scratch (single embedding vector)
  17. GraphͰղ͖͍ͨ՝୊ “ݹయత*”ͳ΋ͷ 36 36 Structured deep models: Deep learning on

    graphs and beyond ׬ᘳͰ͸ͳ͍σʔλͷܽଛ෦෼Λิ׬ͨ͠Γɺ৽͍͠σʔλΛ෼ྨͨ͠Γ *Kipfᐌ͘classical
  18. ϨγϐΛάϥϑʹམͱ͠ࠐΉ ɹϊʔυ: ࡐྉ ɹΤοδ: खॱ Ԡ༻ͱͯ͠ҎԼͷ࣮ݱΛݟਾ͍͑ͯΔ ɾதؒϊʔυͷݕࡧʢᖱΊͨϕʔίϯʣ ɾෳ਺ϨγϐΛϚʔδ ɾ৽͍͠ௐཧ๏ͷ୳ࡧ 2019/02/27ͷൃදͰ෼ྔͷਖ਼نԽ

    ࣮ࣾձͷσʔλΛѻ͏͠ΜͲΈʹγϯύγʔ Machine Readable Recipe (recipe) 41 41 https://speakerdeck.com/ioki/development-of-mrr-at-cookpadtechconf2019
  19. SCIERC (scientific protocol) 42 42 Պֶ࿦จʹରͯ͠entities, relations, coreferencesΛΞϊςʔγϣϯͨ͠σʔλ ηοτΛ࡞੒ طଘͷϞσϧ͕ݸผͷλεΫΛύΠϓϥΠϯ

    తʹܨ͍Ͱͨͷʹର͠ɺmulti-task learning frameworkΛ࣮૷ͨ͠ υϝΠϯݻ༗ͷಛ௃ྔΛ༻͍ͣʹैདྷख๏Λ ্ճΔநग़ਫ਼౓Λ࣮ݱ σʔληοτ ɾ500ຊ෼ͷAbstract ɾentity: 6 types ɾrelation: 7 types Պֶత಺༰ʹಛԽͨ͠৘ใநग़Λ໨ࢦ͢ Luan et al., 2018
  20. PaperRobot (knowledge graph) 43 43 աڈͷ࿦จ͔Β knowldge graph*Λੜ੒ ɹnode: entities/concepts

    ɹedge: relations * άϥϑߏ଄knowledge base ɹʢent1, rel, ent2ʣͷtripletͰදݱ ৽͍͠Պֶతൃݟͱ͸ ৽͍͠node/relationͷൃݟͱଊ͑Δ = incremental work ೖྗ͞ΕͨλΠτϧʹԠͨ֓͡ཁ/݁࿦/ల๬Λࣗಈੜ੒͢Δ ͞Βʹੜ੒͞Εͨ࿦จ͔Β৽͍͠࿦จΛੜ੒͠ɺࣗಈͰແݶʹݚڀΛ͢Δͱ͍͏ߏ૝ 2019/5/20ʹग़ͨϗϠϗϠ࿦จ Wang et al., 2019 SCIERCͷ1st authorͷYi Luan͞Μ͕last author
  21. Learning to Represent Programs with Graphs (source code) 44 44

    ιʔείʔυͷsemanticͳ෼ੳ NLPతͳΞϓϩʔνͰ͸ಉ͡ม਺΍ؔ ਺͕཭Εͨͱ͜ΖͰ࢖ΘΕͨ࣌ͷґଘ ؔ܎ΛղܾͰ͖ͳ͔ͬͨ Gated Graph Neural NetworksΛར༻ Allamanis et al., 2018 VarNaming: จ຺͔Βม਺Λ໋໊ VarMisuse: ม਺ͷޡ༻Λݕ஌
  22. ͓͢͢Ίblogهࣄ 45 45 Graph ConvolutionΛࣗવݴޠॲཧʹԠ༻͢Δʢશ7ճʣ ٕज़ಋೖʹ͓͚ΔԾઆݕূͷϓϩηεΛΞ΢τϓοτɺͱͯ΋ษڧʹͳΔ Part7: ߟ࡯ ɾTransformerͷSelf-AttentionͷΑ͏ʹɺࣗવݴޠॲཧʹ͓͍ͯάϥϑతͳػߏ͕༗༻ͳ৔ ߹͸͋Δɻ͔͠͠ɺͦͷ͜ͱͱGraph

    Convolution͕༗ޮͳ͜ͱ͸౳ՁͰ͸ͳ͍ɻ ɾGraph Convolution͕༗ޮͳͷ͸ɺϊʔυ෼ྨ/άϥϑߏ଄෼ྨͷλεΫʹམͱ͠ࠐΊΔ έʔεɻจ຺৘ใΛ֫ಘ͍ͨ͠ͱ͍͏ϞνϕʔγϣϯͱɺGraph ConvolutionͷಘҙྖҬͱ ͸͋·Γ߹க͠ͳ͍ɻ ɾGraph ConvolutionΛࣗવݴޠॲཧͰ࢖͏ͳΒ͹ɺʮେن໛͔ͭHeterogeneousͳάϥϑ Ͱɺϊʔυ෼ྨͷ໰୊ʹؼணͰ͖Δʯέʔε͕ద͍ͯ͠ΔͱࢥΘΕΔɻ
  23. MoleculeNet (chemical) 47 47 molecular machine learningͷϕϯ νϚʔΫͱͳΔσʔληοτ ྔࢠྗֶɾ෺ཧԽֶɾੜ෺෺ཧֶɾ ੜཧֶͷ؍఺͔ΒूΊΒΕͨ

    700,000ͷԽ߹෺σʔλͰߏ੒ ෳ਺ͷGNNs͕σϑΥϧτͰ࢖͑Δ drug discoveryϑϨʔϜϫʔΫͷ DeepChem಺ʹͯσʔληοτͱ͠ ͯ࢖ΘΕ͍ͯΔ Wu et al., 2018
  24. 57 /-1 .33 .BDIJOF3FBEBCMF3FDJQF ͷ։ൃ%FWFMPQNFOUPG.33!$PPLQBE5FDI$POG 4$*&3$ 1BQFS3PCPU -FBSOJOHUP3FQSFTFOU1SPHSBNTXJUI(SBQIT (SBQI$POWPMVUJPOͰࣗવݴޠॲཧΛߦ͏ 0UIFS"QQMJDBUJPOT

    .PMFDVMF/FU #SBJO/FU$// ࣮ࡍ(//Tͬͯ࢖͑Δͷʁ )PX1PXFSGVMBSF(SBQI/FVSBM/FUXPSLT  8FJTGFJMFSBOE-FNBO(P/FVSBM)JHIFSPSEFS(SBQI/FVSBM/FUXPSLT 3FWJTJUJOH(SBQI/FVSBM/FUXPSLT"MM8F)BWFJT-PX1BTT'JMUFST (SBQI/FVSBM/FUXPSL3FUSJFWB5&$)#-0( 5XJUUFS!TIJPO@IPOEB IUUQTHJUIVCDPNBS9JW5JNFTBS9JW5JNFTJTTVFT References 2/2
  25. 59 59 ࣍ճҎ߱ͷൃදऀΛืू͍ͯ͠·͢ʂ ɾGraph theoryͷਂ۷Γ ɾGNNsͷαʔϕΠ, ࣮૷ ɾϑϨʔϜϫʔΫ, ϥΠϒϥϦ, σʔληοτͷ঺հ

    ɾ෼ੳ݁Ռͷใࠂ ɾͦͷଞͳΜͰ΋ ख఻͍͚ͬͯͨͩΔํ͸SlackͳΓTwitterͳΓͰ ࢁాʢroy29fukuʣ·Ͱ࿈བྷ͍ͩ͘͞ʂ ཁ๬͕͋Ε͹࣍ճҎ߱ଓ͖·͢