Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
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
Search
saltcooky
May 25, 2019
Science
0
730
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
saltcooky
May 25, 2019
Tweet
Share
More Decks by saltcooky
See All by saltcooky
FIBA W杯の日本代表って組み合わせ次第で2次ラウンド行けたんじゃね?をデータで検証
saltcooky12
0
230
Rで有名絵画を安全に買いたい
saltcooky12
0
260
階層クラスタリングにおける仮説検定
saltcooky12
0
870
データドリブンな仮説検証のためのSelective Inference
saltcooky12
1
1.1k
Other Decks in Science
See All in Science
Mastering Feature Engineering: Mining the Hidden Salary Formula with CakeResume
tlyu0419
0
210
Machine Learning for Materials (Lecture 7)
aronwalsh
0
770
東大・松尾研主催 LLM Summer 2023 コンペ解法 (11位 – 20位枠での優秀賞)
hayataka88
0
270
ultraArmをモニター提供してもらった話
miura55
0
150
拡散モデルの概要 −§2. スコアベースモデルについて−
nearme_tech
PRO
0
220
早わかり W3C Community Group
takanorip
0
330
Machine Learning for Materials (Lecture 1)
aronwalsh
1
1.7k
AI(人工知能)の過去・現在・未来 —AIは人間を超えるのか—
tagtag
0
270
DEIM2024 チュートリアル ~AWSで生成AIのRAGを使ったチャットボットを作ってみよう~
yamahiro
3
1k
Machine Learning for Materials (Lecture 5)
aronwalsh
0
610
ざっと学んでみる確率過程 〜その1 : ブラウン運動〜
nearme_tech
PRO
0
220
BigQueryで参加するレコメンドコンペ / bq-recommend-competition-kaggle-meetup-tokyo-2023
shimacos
1
1.6k
Featured
See All Featured
Debugging Ruby Performance
tmm1
71
11k
Making Projects Easy
brettharned
111
5.7k
The Straight Up "How To Draw Better" Workshop
denniskardys
229
130k
Clear Off the Table
cherdarchuk
89
320k
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
155
14k
For a Future-Friendly Web
brad_frost
173
9.2k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
662
120k
The Mythical Team-Month
searls
217
43k
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
35
6.3k
What the flash - Photography Introduction
edds
65
11k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
23
1.9k
How to name files
jennybc
67
96k
Transcript
ετϦʔτεφοϓσʔλʹ ౷ܭతωοτϫʔΫੳͷద༻ΛࢼΈͨ 5PLZP3 !TBMUDPPLZ
୭ʁ • !TBMUDPPLZ • 3ྺɿ͙Β͍͔ͳ • ۈઌɿݪ॓ʹ͋Δ*5ܥͷձࣾ • ࣄ༰ɿ3%తͳ෦ॺͰ3Λͬͨ ɾσʔλੳ
ׂ ɾػցֶश ׂ ɾલॲཧ ׂ • झຯɿϑΝογϣϯඒज़ؗ८Γ
ωοτϫʔΫੳͱ ਓؒؔɺاۀؒͷؔɺੜؒͷؔɺίϯϐϡʔλωοτϫʔΫ ͳͲͷؔߏΛੳ͢ΔάϥϑཧΛϕʔεͱͨ͠ੳख๏ (ग़య : https://www.slideshare.net/kashitan/tidygraphggraph) (https://www.amazon.co.jp/exec/obidos/ASIN/4320019288) ͜ΕͰษڧ͠·ͨ͠ ࠷ۙͷTokyoRͩͱ @kashitan
͞Μ͕ ൃදͨ͠Γͯͨ͠
ωοτϫʔΫੳ Α͋͘ΔͷωοτϫʔΫͷࢦඪͷࢉग़ߏͷநग़ - த৺ੑ ͲͷΑ͏ͳਓ͕த৺తͳਓ͔ - ίϛϡχςΟநग़ ͲͷΑ͏ͳάϧʔϓʹ͔Ε͍ͯΔ͔ - ૬ؔ
̎ͭͷωοτϫʔΫࣅ͍ͯΔ͔Ͳ͏͔ - ίΞநग़ ωοτϫʔΫͷີʹ݁߹ͨ͠த৺෦
ωοτϫʔΫͷ͋Δ̎ͷؒ J K ͷลɺ֬QJKͰ֬తʹൃੜ͢Δͱߟ͑Δ QJKύϥϝʔλВΛ࣋ͭϩδεςΟοΫϞσϧͰදݱͰ͖Δ J KͱK Lʹล͕ுΔ֬QJKºQKLͱදݱͰ͖Δ ౷ܭతωοτϫʔΫੳ K
L J
ࢦϥϯμϜάϥϑϞσϧ FYQPOFOUJBMSBOEPNHSBQINPEFM ɹϥϯμϜάϥϑ:ʹ͓͍ͯಛఆͷάϥϑߏZ͕ಘΒΕΔ֤֬ล͕ுΔ֬ͷ ྦྷͰදݱͰ͖Δͱߟ͑ͨϞσϧ ౷ܭతωοτϫʔΫੳ yʹ͋Δลͷ ύϥϝʔλ ن֨Խఆ ωοτϫʔΫશମ
ͷลͷൃੜ֬
ࢦϥϯμϜάϥϑϞσϧɹQ Ϟσϧ ɹϥϯμϜάϥϑ:ͷลͷൃੜ༷֬ʑͳཁૉʹΑΓ֬తʹܾ·ΔϞσϧ ౷ܭతωοτϫʔΫੳ ཁૉ ϊʔυͷಛྔɿྸɺॏΈɺ෦ॺʜ ลͷಛྔɿަࡍظؒɺΈʜ ϊʔυؒͷؔͷಛɿྸࠩɺۈଓظؒࠩʜ ߏతͳಛྔɿLελʔߏͷʜ ωοτϫʔΫͷߏཁ
ཁૉͷ
ద༻σʔλ
ద༻σʔλ ྸ ৬ۀ ࡱӨॴ ண༻ϒϥϯυ
Ϟνϕʔγϣϯ ลண༻ϒϥϯυͷ ڞ௨ ϒϥϯυͷબͷੑ࣭Λ දݱͰ͖ͳ͍͔ (͔ͳΓແཧཧ)
σʔλऔಘ • ($1্Ͱ%PDLFSΛ༻͍ͯ3TUVEJP 34FMFOJVNڥΛ࡞ • SWFTUQBDLBHFΛར༻ͯ͠εΫϨΠϐϯά • ϙΞιϯʹै͏ִؒͰϖʔδऔಘ ͳΜͱͳ͘
• ҰਓͷεφοϓσʔλΛऔಘ
σʔλ֬ೝ ண༻ϒϥϯυϥϯΩϯά ண༻ϒϥϯυωοτϫʔΫ
Ϟσϧ࡞(ྫ) ࢦϥϯμϜϞσϧTUBUOFUQBDLBHFͰ࣮͕Ͱ͖·͢ɻ # ωοτϫʔΫΦϒδΣΫτͷ࡞ network <- as.network(x = graph_matrix, directed
= FALSE, loops = FALSE) # ֤Τοδʹઆ໌ม(ྸ)ΛՃ network %v% "Age" <- Age # ֤ΤοδͷྸͷࠩΛܭࢉ diff.age <- abs(sweep(matrix(snap_info$Age, nrow = 638, ncol = 638), 2, snap_info$Age)) # Ϟσϧ࡞ model <- ergm( network ~ edges + edgecov(diff.age) + nodecov(“Age”) )
Ϟσϧ࡞ ࢦϥϯμϜϞσϧTUBUOFUQBDLBHFͰ࣮͕Ͱ͖·͢ɻ # ετϦʔτεφοϓͷp*Ϟσϧੜ snap_net_model <- ergm(snap_net ~ edges
+ # ลͷ nodecov(“Age")+ # ྸࠩ edgecov(diff.age) + # ྸ nodematch(“Occupation”) + # ৬ۀ nodematch("Point") ) # ࡱӨॴ
݁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.) ࡱӨॴ͕ลͷൃੜʹ Өڹ͍ͯͦ͠͏ AIC/BICͰมબՄೳ
݁ՌΛݟͯΈΔ ϞσϧΛ༻͍ͯγϛϡϨʔγϣϯ ࣮ઢɿγϛϡϨʔγϣϯʹΑΔ ശͻ͛ਤɿ࣮σʔλͷ ͯ·Γྑ͘ͳ͍ʜ
·ͱΊ • ࠓճͷεφοϓใͰɺண༻ϒϥϯυͷؔੑΛࢦϥϯμϜ άϥϑϞσϧͰ͏·͘දݱͰ͖·ͤΜͰͨ͠ • ౷ܭతωοτϫʔΫੳ݁ߏ໘ന͍ͷͰɺษڧͯ͠ΈͯͶ • ࢲ౷ܭతωοτϫʔΫੳͷษڧଓ͚͍͖͍ͯͨͱࢥ͍·͢ • ͳͷͰɺৄ͍͠ํ͝ڭतئ͍͠·͢
• ڞཱग़൛ʮωοτϫʔΫੳୈ̎൛ʯླஶ IUUQTXXXBNB[PODPKQFYFDPCJEPT"4*/ • \UJEZHSBQI^ͱ\HHSBQI^ʹΑΔϞμϯͳωοτϫʔΫੳ IUUQTXXXTMJEFTIBSFOFULBTIJUBOUJEZHSBQIHHSBQI • 3ʹΑΔωοτϫʔΫੳΛ·ͱΊ·ͨ͠ωοτϫʔΫͷࢦඪฤ IUUQTRJJUBDPNTBMUDPPLZJUFNTFEDFEGCDE •
3ʹΑΔωοτϫʔΫੳΛ·ͱΊ·ͨ͠౷ܭతωοτϫʔΫੳฤ IUUQTRJJUBDPNTBMUDPPLZJUFNTCBFGDFCGBDFBDCGD ࢀߟ