ストリートスナップデータに 統計的ネットワーク分析の適用を試みた

TokyoR #78 LT

Transcript

1. ετϦʔτεφοϓσʔλʹ
 ౷ܭతωοτϫʔΫ෼ੳͷద༻ΛࢼΈͨ 5PLZP3
   
   
 !TBMUDPPLZ
2. ୭ʁ • !TBMUDPPLZ
   • 3ྺɿ೥͙Β͍͔ͳ
   • ۈઌɿݪ॓ʹ͋Δ*5ܥͷձࣾ
   • ࢓ࣄ಺༰ɿ3%తͳ෦ॺͰ3Λ࢖ͬͨ

   
   
   
   ɾσʔλ෼ੳ ׂ 

   
   
   
   ɾػցֶश ׂ 

   
   
   
   ɾલॲཧ ׂ
   • झຯɿ෰ϑΝογϣϯඒज़ؗ८Γ
3. ωοτϫʔΫ෼ੳͱ͸ ਓؒؔ܎ɺاۀؒͷؔ܎ɺੜ෺ؒͷؔ܎ɺίϯϐϡʔλωοτϫʔΫ ͳͲͷؔ܎΍ߏ଄Λ෼ੳ͢Δάϥϑཧ࿦Λϕʔεͱͨ͠෼ੳख๏
   (ग़య : https://www.slideshare.net/kashitan/tidygraphggraph) (https://www.amazon.co.jp/exec/obidos/ASIN/4320019288) ͜ΕͰษڧ͠·ͨ͠ ࠷ۙͷTokyoRͩͱ
 @kashitan ͞Μ͕ ൃදͨ͠Γͯͨ͠
4. ωοτϫʔΫ෼ੳ Α͋͘Δͷ͸ωοτϫʔΫͷࢦඪͷࢉग़΍ߏ଄ͷநग़ - த৺ੑ

   
   ͲͷΑ͏ͳਓ͕த৺తͳਓ෺͔
   - ίϛϡχςΟநग़

   
   ͲͷΑ͏ͳάϧʔϓʹ෼͔Ε͍ͯΔ͔
   - ૬ؔ܎਺

   
   ̎ͭͷωοτϫʔΫ͸ࣅ͍ͯΔ͔Ͳ͏͔
   - ίΞநग़

   
   ωοτϫʔΫͷີʹ݁߹ͨ͠த৺෦෼
5. 
   ωοτϫʔΫͷ͋Δ̎఺ͷ௖఺ؒ J
   
   K ͷล͸ɺ֬཰QJKͰ֬཰తʹൃੜ͢Δͱߟ͑Δ
   
   QJK͸ύϥϝʔλВΛ࣋ͭϩδεςΟοΫϞσϧͰදݱͰ͖Δ
   
   ௖఺
   J
   Kͱ௖఺
   K
   Lʹล͕ுΔ֬཰͸QJKºQKLͱදݱͰ͖Δ ౷ܭతωοτϫʔΫ෼ੳ K L J
6. ࢦ਺ϥϯμϜάϥϑϞσϧ FYQPOFOUJBM
   SBOEPN
   HSBQI
   NPEFM
   ɹ
   ϥϯμϜάϥϑ:ʹ͓͍ͯಛఆͷάϥϑߏ଄Z͕ಘΒΕΔ֬཰͸֤ล͕ுΔ֬཰ͷ

   
   
   
   
   ྦྷ৐ͰදݱͰ͖Δͱߟ͑ͨϞσϧ
   ౷ܭతωοτϫʔΫ෼ੳ yʹ͋Δลͷ਺ ύϥϝʔλ ن֨Խఆ਺ ωοτϫʔΫશମ ͷลͷൃੜ֬཰
7. ࢦ਺ϥϯμϜάϥϑϞσϧɹQ Ϟσϧ
   ɹϥϯμϜάϥϑ:ͷลͷൃੜ֬཰͸༷ʑͳཁૉʹΑΓ֬཰తʹܾ·ΔϞσϧ ౷ܭతωοτϫʔΫ෼ੳ 
   ཁૉ
   
   
   
   ϊʔυͷಛ௃ྔɿ೥ྸɺॏΈɺ෦ॺʜ
   
   ลͷಛ௃ྔɿަࡍظؒɺ޷Έʜ
   
   ϊʔυؒͷؔ܎ͷಛ௃ɿ೥ྸࠩɺۈଓظؒࠩʜ
   
   ߏ଄తͳಛ௃ྔɿLελʔߏ଄ͷ਺ʜ ωοτϫʔΫͷߏ੒ཁ ཁૉͷ਺
8. ద༻σʔλ
9. ద༻σʔλ ೥ྸ ৬ۀ ࡱӨ৔ॴ ண༻ϒϥϯυ
10. Ϟνϕʔγϣϯ ล͸ண༻ϒϥϯυͷ ڞ௨౓ ϒϥϯυͷબ୒ͷੑ࣭Λ දݱͰ͖ͳ͍͔ (͔ͳΓແཧ໼ཧ)
11. σʔλऔಘ • ($1্Ͱ%PDLFSΛ༻͍ͯ3TUVEJP
    
    34FMFOJVN؀ڥΛ࡞੒
    • SWFTU
    QBDLBHFΛར༻ͯ͠εΫϨΠϐϯά
    • ϙΞιϯ෼෍ʹै͏ִؒͰϖʔδऔಘ ͳΜͱͳ͘
    • ໿Ұ೥෼
    ਓͷεφοϓσʔλΛऔಘ
12. σʔλ֬ೝ ண༻ϒϥϯυϥϯΩϯά ண༻ϒϥϯυωοτϫʔΫ
13. Ϟσϧ࡞੒(ྫ) ࢦ਺ϥϯμϜϞσϧ͸TUBUOFU
    QBDLBHFͰ࣮૷͕Ͱ͖·͢ɻ # ωοτϫʔΫΦϒδΣΫτͷ࡞੒
 network <- as.network(x = graph_matrix, directed = FALSE, loops = FALSE) # ֤Τοδʹઆ໌ม਺(೥ྸ)Λ௥Ճ network %v% "Age" <- Age # ֤Τοδͷ೥ྸͷࠩΛܭࢉ diff.age <- abs(sweep(matrix(snap_info$Age, nrow = 638, ncol = 638), 2, snap_info$Age)) # Ϟσϧ࡞੒
 model <- ergm( network ~ edges + edgecov(diff.age) + nodecov(“Age”) )

14. Ϟσϧ࡞੒ ࢦ਺ϥϯμϜϞσϧ͸TUBUOFU
    QBDLBHFͰ࣮૷͕Ͱ͖·͢ɻ # ετϦʔτεφοϓͷp*Ϟσϧੜ੒ snap_net_model <- ergm(snap_net ~ 
 edges + # ลͷ਺ nodecov(“Age")+ # ೥ྸࠩ edgecov(diff.age) + # ೥ྸ nodematch(“Occupation”) + # ৬ۀ nodematch("Point") ) # ࡱӨ৔ॴ

15. ݁ՌΛݟͯΈΔ > summary(snap_net_model) < ུ > Monte Carlo MLE Results: Estimate Std. Error MCMC % z value Pr(>|z|) edges -5.2066393 0.2692526 0 -19.337 <1e-04 *** edgecov.diff.age -0.0015763 0.0094767 0 -0.166 0.8679 nodecov.Age -0.0003136 0.0061215 0 -0.051 0.9591 nodematch.Occupation -0.0453192 0.0842853 0 -0.538 0.5908 nodematch.Point 0.1491330 0.0628610 0 2.372 0.0177 * 
 < ུ > AIC: 13485 BIC: 13536 (Smaller is better.)
 ࡱӨ৔ॴ͕ลͷൃੜʹ Өڹ͍ͯͦ͠͏ AIC/BICͰม਺બ୒Մೳ
16. ݁ՌΛݟͯΈΔ ϞσϧΛ༻͍ͯγϛϡϨʔγϣϯ
    
    
    ࣮ઢɿγϛϡϨʔγϣϯʹΑΔ஋

    
    ശͻ͛ਤɿ࣮σʔλͷ஋
    ౰ͯ͸·Γྑ͘ͳ͍ʜ
17. ·ͱΊ • ࠓճͷεφοϓ৘ใͰ͸ɺண༻ϒϥϯυͷؔ܎ੑΛࢦ਺ϥϯμϜ άϥϑϞσϧͰ͏·͘දݱͰ͖·ͤΜͰͨ͠
    • ౷ܭతωοτϫʔΫ෼ੳ͸݁ߏ໘ന͍ͷͰɺษڧͯ͠ΈͯͶ
    • ࢲ΋౷ܭతωοτϫʔΫ෼ੳͷษڧଓ͚͍͖͍ͯͨͱࢥ͍·͢
    • ͳͷͰɺৄ͍͠ํ͸͝ڭतئ͍͠·͢
18. • ڞཱग़൛ʮωοτϫʔΫ෼ੳ
    ୈ̎൛ʯླ໦
    ౒
    ஶ
 IUUQTXXXBNB[PODPKQFYFDPCJEPT"4*/
    • \UJEZHSBQI^ͱ\HHSBQI^ʹΑΔϞμϯͳωοτϫʔΫ෼ੳ
 IUUQTXXXTMJEFTIBSFOFULBTIJUBOUJEZHSBQIHHSBQI
    • 3ʹΑΔωοτϫʔΫ෼ੳΛ·ͱΊ·ͨ͠ωοτϫʔΫͷࢦඪฤ
 IUUQTRJJUBDPNTBMUDPPLZJUFNTFEDFEGCDE
    • 3ʹΑΔωοτϫʔΫ෼ੳΛ·ͱΊ·ͨ͠౷ܭతωοτϫʔΫ෼ੳฤ
 IUUQTRJJUBDPNTBMUDPPLZJUFNTCBFGDFCGBDFBDCGD ࢀߟ