untitled

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "untitled"

Transcription

1 Amazon.co.jp START

2 Amazon.co.jp Amazon.co.jp Amazon.co.jp Amazon Internet retailers are extremely hesitant about releasing specific sales data 1( )

3 ranking 500, ,000 Jan.1 Mar.1 Jun.1 Sep.1 Dec Amazon.co.jp 2( )

4 Stochastic ranking process a. b. 1 3( )

5 Stochastic ranking process a. b. 1 Amazon Amazon.co.jp Stochastic ranking process Pareto Amazon.co.jp 4( )

6 Amazon.co.jp Stochastic ranking process 5( )

7 Stochastic ranking process N ;1, 2,,N x (N) 1,0 w (N),,x(N) N,0 1,,w (N) N X (N) 1 (t),,x (N) ; 1 jump N (t) t [ 0] X (N) i (0) = x (N) i,0 ( i) τ (N) i,j, i =1, 2,,N, j =1, 2, ; i j 1 jump j τ (N) i,j+1 τ (N) i,j, j =0, 1, 2, (τ (N) i,0 =0) i, j j P[ τ (N) i,1 t ]=1 e w(n) i t 6( )

8 [ 1] X (N) i (τ (N) i,j )=1( i, j) [ 2] X (N) i (τ (N) i,j )=X (N) i jump 1 jump (τ (N) i,j 0) + 1 ( i, i,j ) t=τ 1,1 t=τ 2,1 t=τ 1,2 t=τ 3, τ 1,1 <τ 2,1 <τ 1,2 <τ 3,1 < 7( )

9 x C (t) jump jump x C (t) jump jump x C (t) x (N) N C (t) =1+ χ (N) τ i=1 i t 1 t =0 X (N) C (t) =1 8( )

10 x C (t) N Jump λ (N) = 1 N y (N) C y C (t) =1 0 N i=1 δ w (N) i (t) = 1 N (x(n) C (t) 1) = 1 N e wt λ(dw) N λ N i=1 χ (N) τ i t y C (t) jump λ 9( )

11 y (N) i,0 = 1 N (x(n) i,0 1) N μ (N) y,0 (dw dy) = 1 N μ (N) y,t i δ w (N) i (dw) δ (N)(dy) μ y y,0 (dw) dy (N ) i,0 = 1 N (X(N) i Jump Y (N) i := 1 δ (N) N w i i μ y,t (dw) dy 1) δ (N) Y i (t) N μ y,t (dw) 10( )

12 U i t (y, t)+ j f j U j (y, t) U i y (y, t) = f iu i (y, t) (y, t) [0, 1) [0, ) 1 Burgers f i 0 i U i (y, t) t y i f j U j (0, 0) <, U i (y, 0) 0, smooth, j U j (y, 0) = 1 y Burgers j U i (0,t)=U i (0, 0), t 0 dy B dt (t) =v(y B(t),t); v(y, t) = j f j U j (y, t), y B (0) = y 0 jump μ y,t ({f i })= U i (y, t) y 11( )

13 jump U (dw; y, t)+ w U(dw ; y, t) U (dw; y, t) = wu(dw; y, t), t y (y, t) [0, 1) [0, ) U(dw; y, 0) 0 smooth, in y wu(dw;0, 0) <, U(R + ; y, 0) = 1 y Burgers U(dw;0,t)=U(dw;0, 0), t 0 μ y,t (dw) = U (dw; y, t) y 12( )

14 jump tail N Amazon.co.jp PDE 13( )

15 t =0 x C (t) Ny C (t) =N N 0 e wt λ(dw) Jump λ Pareto 14( )

16 Jump Pareto ( ) N 1/b Pareto w i = a, i =1,,N, a, b > 0 i w i i a: = an 1/b b b b x C (t) Ny C (t) N(1 b(at) b Γ( b, at)) Γ N, a, b 15( )

17 (n d 1 = 77) N =90 a = (1/a =3.5 ) b = ( χ 2 /n d =1 ) ( )

18 ( )

19 Chevalier Goolsbee b =1.2 Online bookstore brick-and-mortar bookstore CPI Brynjolfsson Hu Smith b =1.148 J. A. Hausman (1997) (consumer welfare) Long tail 0 input b Online retail Amazon.co.jp 18( )

20 y C (t) μ(dw; y, t) λ Pareto 0 <r<1 r S(r, 1) N (w,z) [0, ) [r,1) wμ z,t (dw) dz = NabΓ(1 b, q(r)) q(r) b 1 ; q(r) =at 1 (r), r =1 e q(r) + q(r) b Γ(1 b, q(r)) cf. w i 1 r S(r, 1) N ab b 1 (1 r(b 1)/b ) S(r, 1) (b =1.15, 1.2) S(r, 1) ( )

21 b b b b>1: S tot = S(0, 1) Nab b 1 S(0, 0.2) b =2: S tot S(0.2, 1) b =1.2 (Chevalier, Goolsbee) 0.235, S tot S(0.2, 1) b =1.15 (Brynjolfsson, Hu, Smith) S tot N b<1: S(r, 1) Nab b 1 (1 r(b 1)/b ): r =0 Amazon.co.jp b = < 1 20( )

22 Amazon.co.jp C. Anderson, The Long tail Amazon.co.jp: b = < 1 Amazon Amazon Amazon 21( )

23 2ch.net (2ch.net) web 1 stochastic ranking process 22( )

24 ranking N = 795, (a,b ) = ( , 0.62) (1/a = 4 ) 12:00 24:00 time ( )

25 stochastic ranking process Amazonl.co.jp λ (stochastic ranking process) online retail, long tail 24( )

26 web activity 25( )

27 8 λ stochastic ranking process 26( )

28 Stochastic ranking process (?) Stochastic ranking process long tail 27( )

29 NHK No.2592 Citation Statistics, (2008.6) International Mathematical Union (IMU), ICIAM, IMS citation data use and misuse C. Anderson (long tail) Long tail 28( )

30 impact factor 29( )

31 Stochastic ranking process ( )

32 End of slides. Click [END] to finish the presentation. K. Hattori, T. Hattori, Existence of an infinite particle limit of stochastic ranking process, Stochastic Processes and their Applications (2008), to appear. K. Hattori, T. Hattori, Equation of motion for incompressible mixed fluid driven by evaporation and its application to online rankings, preprint (2007). K. Hattori, T. Hattori, Mathematical analysis of long tail economy using stochastic ranking processes, preprint (2008). Google END Bye

467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 B =(1+R ) B +G τ C C G τ R B C = a R +a W W ρ W =(1+R ) B +(1+R +δ ) (1 ρ) L B L δ B = λ B + μ (W C λ B )

More information

第86回日本感染症学会総会学術集会後抄録(II)

第86回日本感染症学会総会学術集会後抄録(II) χ μ μ μ μ β β μ μ μ μ β μ μ μ β β β α β β β λ Ι β μ μ β Δ Δ Δ Δ Δ μ μ α φ φ φ α γ φ φ γ φ φ γ γδ φ γδ γ φ φ φ φ φ φ φ φ φ φ φ φ φ α γ γ γ α α α α α γ γ γ γ γ γ γ α γ α γ γ μ μ κ κ α α α β α

More information

,.,.,,. [15],.,.,,., 2003 3 2006 2 3. 2003 3 2004 2 2004 3 2005 2, 1., 2005 3 2006 2, 1., 1,., 1,,., 1. i

,.,.,,. [15],.,.,,., 2003 3 2006 2 3. 2003 3 2004 2 2004 3 2005 2, 1., 2005 3 2006 2, 1., 1,., 1,,., 1. i 200520866 ( ) 19 1 ,.,.,,. [15],.,.,,., 2003 3 2006 2 3. 2003 3 2004 2 2004 3 2005 2, 1., 2005 3 2006 2, 1., 1,., 1,,., 1. i 1 1 1.1..................................... 1 1.2...................................

More information

求人面接資料PPT

求人面接資料PPT Hair Salon TV etc. 250" 250" 200" 200" 150" 150" 100" 100" 50" 50" 0" 0" Nov)13" Dec)13" Jan)14" Feb)14" Mar)14" Apr)14" May)14" Jun)14" Jul)14" Dec)12" Jan)13" Feb)13" Mar)13" Apr)13"

More information

Copyrght 7 Mzuho-DL Fnancal Technology Co., Ltd. All rghts reserved.

Copyrght 7 Mzuho-DL Fnancal Technology Co., Ltd. All rghts reserved. 766 Copyrght 7 Mzuho-DL Fnancal Technology Co., Ltd. All rghts reserved. Copyrght 7 Mzuho-DL Fnancal Technology Co., Ltd. All rghts reserved. 3 Copyrght 7 Mzuho-DL Fnancal Technology Co., Ltd. All rghts

More information

一般演題(ポスター)

一般演題(ポスター) 6 5 13 : 00 14 : 00 A μ 13 : 00 14 : 00 A β β β 13 : 00 14 : 00 A 13 : 00 14 : 00 A 13 : 00 14 : 00 A β 13 : 00 14 : 00 A β 13 : 00 14 : 00 A 13 : 00 14 : 00 A β 13 : 00 14 : 00 A 13 : 00 14 : 00 A

More information

5 36 5................................................... 36 5................................................... 36 5.3..............................

5 36 5................................................... 36 5................................................... 36 5.3.............................. 9 8 3............................................. 3.......................................... 4.3............................................ 4 5 3 6 3..................................................

More information

日本糖尿病学会誌第58巻第7号

日本糖尿病学会誌第58巻第7号 l l l l β μ l l l l l l α l l l l l l l μ l l l α l l l l l μ l l l l l l l l l l l l l μ l l l l l β l μ l μ l μ l μ l l l l l μ l l l μ l l μ l l l α α l μ l l μ l α l μ l α l l l μ l l l μ l l μ l

More information

0 1-4. 1-5. (1) + b = b +, (2) b = b, (3) + 0 =, (4) 1 =, (5) ( + b) + c = + (b + c), (6) ( b) c = (b c), (7) (b + c) = b + c, (8) ( + b)c = c + bc (9

0 1-4. 1-5. (1) + b = b +, (2) b = b, (3) + 0 =, (4) 1 =, (5) ( + b) + c = + (b + c), (6) ( b) c = (b c), (7) (b + c) = b + c, (8) ( + b)c = c + bc (9 1-1. 1, 2, 3, 4, 5, 6, 7,, 100,, 1000, n, m m m n n 0 n, m m n 1-2. 0 m n m n 0 2 = 1.41421356 π = 3.141516 1-3. 1 0 1-4. 1-5. (1) + b = b +, (2) b = b, (3) + 0 =, (4) 1 =, (5) ( + b) + c = + (b + c),

More information

成形加工第20巻8号 最終/P572‐580 8‐4■■

成形加工第20巻8号 最終/P572‐580 8‐4■■ 572 φ SeikeiKakou Vol. No. 573 574 μ μ μ SeikeiKakou Vol. No. 575 μ μ φ μμ 576 Coatef molten polymer Compression Coating and Cooling Releasing SeikeiKakou Vol. No. 577 λ 578 SeikeiKakou Vol. No. 579 Intern.

More information

日本糖尿病学会誌第58巻第3号

日本糖尿病学会誌第58巻第3号 l l μ l l l l l μ l l l l μ l l l l μ l l l l l l l l l l l l l μ l l l l μ Δ l l l μ Δ μ l l l l μ l l μ l l l l l l l l μ l l l l l μ l l l l l l l l μ l μ l l l l l l l l l l l l μ l l l l β l l l μ

More information

2

2 2 485 1300 1 6 17 18 3 18 18 3 17 () 6 1 2 3 4 1 18 11 27 10001200 705 2 18 12 27 10001230 705 3 19 2 5 10001140 302 5 () 6 280 2 7 ACCESS WEB 8 9 10 11 12 13 14 3 A B C D E 1 Data 13 12 Data 15 9 18 2

More information

チュートリアル:ノンパラメトリックベイズ

チュートリアル:ノンパラメトリックベイズ { x,x, L, xn} 2 p( θ, θ, θ, θ, θ, } { 2 3 4 5 θ6 p( p( { x,x, L, N} 2 x { θ, θ2, θ3, θ4, θ5, θ6} K n p( θ θ n N n θ x N + { x,x, L, N} 2 x { θ, θ2, θ3, θ4, θ5, θ6} log p( 6 n logθ F 6 log p( + λ θ F θ

More information

Home Use Test 1 2

Home Use Test 1 2 C O M P A N Y P R O F I L E Home Use Test Home Use Test 1 2 I n t e r n e t R e s e a r c h M o n i t o r R e c r u i t 1,215 1200 1,087 1000 863 800 682 600 483 400 200 0 2009 2010 2011 2012 2013 Sales

More information

日本糖尿病学会誌第58巻第2号

日本糖尿病学会誌第58巻第2号 β γ Δ Δ β β β l l l l μ l l μ l l l l α l l l ω l Δ l l Δ Δ l l l l l l l l l l l l l l α α α α l l l l l l l l l l l μ l l μ l μ l l μ l l μ l l l μ l l l l l l l μ l β l l μ l l l l α l l μ l l

More information

607_h1h4_0215.indd

607_h1h4_0215.indd 3 2016 Mar. No.607 http://www.saitama-ctv-kyosai.net 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

More information

10.02EWE51号本文

10.02EWE51号本文 51 2010 Mar. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

More information

初めに:

初めに: 2 Copyrightc2008 JETRO. All rights reserved. FAX 03-5572-7044 ...5...6 (1)...7... 11... 11...12...14...15...15...16...17...18 (4)...21 (5)...21 (6)...23 4 Copyrightc2008 JETRO. All rights reserved. 5 Copyrightc2008

More information

2 [] [4] (Preference Model) 2. 2.. [8] 8km/l 3cc 3 c 29 52

2 [] [4] (Preference Model) 2. 2.. [8] 8km/l 3cc 3 c 29 52 Transactions of the Operations Research Society of Japan 29 52-9 ( 26 7 9 ; 28 8 3 ) 25 9 5 :,,,. [5,6,9] SD (Semantic Differential Method) 5 AHP(Analytic Hierarchy Process) 2 [] [4] (Preference Model)

More information

example2_time.eps

example2_time.eps Google (20/08/2 ) ( ) Random Walk & Google Page Rank Agora on Aug. 20 / 67 Introduction ( ) Random Walk & Google Page Rank Agora on Aug. 20 2 / 67 Introduction Google ( ) Random Walk & Google Page Rank

More information

新たな基礎年金制度の構築に向けて

新たな基礎年金制度の構築に向けて [ ] 1 1 4 60 1 ( 1 ) 1 1 1 4 1 1 1 1 1 4 1 2 1 1 1 ( ) 2 1 1 1 1 1 1 1996 1 3 4.3(2) 1997 1 65 1 1 2 1/3 ( )2/3 1 1/3 ( ) 1 1 2 3 2 4 6 2.1 1 2 1 ( ) 13 1 1 1 1 2 2 ( ) ( ) 1 ( ) 60 1 1 2.2 (1) (3) ( 9

More information

ネットショップ・オーナー2 ユーザーマニュアル

ネットショップ・オーナー2  ユーザーマニュアル 1 1-1 1-2 1-3 1-4 1 1-5 2 2-1 A C 2-2 A 2 C D E F G H I 2-3 2-4 2 C D E E A 3 3-1 A 3 A A 3 3 3 3-2 3-3 3-4 3 C 4 4-1 A A 4 B B C D C D E F G 4 H I J K L 4-2 4 C D E B D C A C B D 4 E F B E C 4-3 4

More information

EPSON エプソンプリンタ共通 取扱説明書 ネットワーク編

EPSON エプソンプリンタ共通 取扱説明書 ネットワーク編 K L N K N N N N N N N N N N N N L A B C N N N A AB B C L D N N N N N L N N N A L B N N A B C N L N N N N L N A B C D N N A L N A L B C D N L N A L N B C N N D E F N K G H N A B C A L N N N N D D

More information

ありがとうございました

ありがとうございました - 1 - - 2 - - 3 - - 4 - - 5 - 1 2 AB C A B C - 6 - - 7 - - 8 - 10 1 3 1 10 400 8 9-9 - 2600 1 119 26.44 63 50 15 325.37 131.99 457.36-10 - 5 977 1688 1805 200 7 80-11 - - 12 - - 13 - - 14 - 2-1 - 15 -

More information

EPSON エプソンプリンタ共通 取扱説明書 ネットワーク編

EPSON エプソンプリンタ共通 取扱説明書 ネットワーク編 K L N K N N N N N N N N N N N N L A B C N N N A AB B C L D N N N N N L N N N A L B N N A B C N L N N N N L N A B C D N N A L N A L B C D N L N A L N B C N N D E F N K G H N A B C A L N N N N D D

More information

公務員人件費のシミュレーション分析

公務員人件費のシミュレーション分析 47 50 (a) (b) (c) (7) 11 10 2018 20 2028 16 17 18 19 20 21 22 20 90.1 9.9 20 87.2 12.8 2018 10 17 6.916.0 7.87.4 40.511.6 23 0.0% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2.0% 4.0% 6.0% 8.0%

More information

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 A B (A/B) 1 1,185 17,801 6.66% 2 943 26,598 3.55% 3 3,779 112,231 3.37% 4 8,174 246,350 3.32% 5 671 22,775 2.95% 6 2,606 89,705 2.91% 7 738 25,700 2.87% 8 1,134

More information

橡hashik-f.PDF

橡hashik-f.PDF 1 1 1 11 12 13 2 2 21 22 3 3 3 4 4 8 22 10 23 10 11 11 24 12 12 13 25 14 15 16 18 19 20 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 144 142 140 140 29.7 70.0 0.7 22.1 16.4 13.6 9.3 5.0 2.9 0.0

More information

198

198 197 198 199 200 201 202 A B C D E F G H I J K L 203 204 205 A B 206 A B C D E F 207 208 209 210 211 212 213 214 215 A B 216 217 218 219 220 221 222 223 224 225 226 227 228 229 A B C D 230 231 232 233 A

More information

1

1 1 2 3 4 5 (2,433 ) 4,026 2710 243.3 2728 402.6 6 402.6 402.6 243.3 7 8 20.5 11.5 1.51 0.50.5 1.5 9 10 11 12 13 100 99 4 97 14 A AB A 12 14.615/100 1.096/1000 B B 1.096/1000 300 A1.5 B1.25 24 4,182,500

More information

http://www2.math.kyushu-u.ac.jp/~hara/lectures/lectures-j.html 2 N(ε 1 ) N(ε 2 ) ε 1 ε 2 α ε ε 2 1 n N(ɛ) N ɛ ɛ- (1.1.3) n > N(ɛ) a n α < ɛ n N(ɛ) a n

http://www2.math.kyushu-u.ac.jp/~hara/lectures/lectures-j.html 2 N(ε 1 ) N(ε 2 ) ε 1 ε 2 α ε ε 2 1 n N(ɛ) N ɛ ɛ- (1.1.3) n > N(ɛ) a n α < ɛ n N(ɛ) a n http://www2.math.kyushu-u.ac.jp/~hara/lectures/lectures-j.html 1 1 1.1 ɛ-n 1 ɛ-n lim n a n = α n a n α 2 lim a n = 1 n a k n n k=1 1.1.7 ɛ-n 1.1.1 a n α a n n α lim n a n = α ɛ N(ɛ) n > N(ɛ) a n α < ɛ

More information

0.,,., m Euclid m m. 2.., M., M R 2 ψ. ψ,, R 2 M.,, (x 1 (),, x m ()) R m. 2 M, R f. M (x 1,, x m ), f (x 1,, x m ) f(x 1,, x m ). f ( ). x i : M R.,,

0.,,., m Euclid m m. 2.., M., M R 2 ψ. ψ,, R 2 M.,, (x 1 (),, x m ()) R m. 2 M, R f. M (x 1,, x m ), f (x 1,, x m ) f(x 1,, x m ). f ( ). x i : M R.,, 2012 10 13 1,,,.,,.,.,,. 2?.,,. 1,, 1. (θ, φ), θ, φ (0, π),, (0, 2π). 1 0.,,., m Euclid m m. 2.., M., M R 2 ψ. ψ,, R 2 M.,, (x 1 (),, x m ()) R m. 2 M, R f. M (x 1,, x m ), f (x 1,, x m ) f(x 1,, x m ).

More information

研究シリーズ 第34号

研究シリーズ 第34号 personal income distribution 64 life stage 4134 (R.E.Mouer) 21 38 32 1 30 2 37 44 45 3 65 1 30 1. 12 3 4 5 4 8 5 2 28 1 37 38 5 1 41 34 2 30 4 2 5 38 66 38 2 40 38 6 1 1 5 3 34 67 12 3 31 3 52 8 3 1 1

More information

2015壺溪塾表1表4_0105

2015壺溪塾表1表4_0105 KOKEI JUKU GUIDANCE FOR SUCCESS ? ? ! ?! Courses Guide 前 年 度 合 格 校 : 東 京 大 学 / 京 都 大 学 / 一 橋 大 学 / 東 京 工 業 大 学 / 大 阪 大 学 など 卒 塾 生 の 声 卒 塾 生 の 声 Courses Guide 前 年 度 合 格 校 : 九 州 大 学 / 東 京 工 業 大 学 / 大

More information

untitled

untitled -1- 12 3 7 8 3.5 ( ) 7 8 3 1 2 3 ( ) ( -2- 3 7 5 21 2 2-3- 2 2 18 ( 1) ( 1) ( ) 6. 9 ( 9 15 ) 7. 2 ( 9 ) ( 11 32 11 ) 11. 4 20. 10 ( 26 15 21 ) 23. 9 28. 4 ( 8 ) ( 34 ) 32. 9 ( 45 ) 32. 12 ( 10 13 ) 33.

More information

1926

1926 2010 10 2011 2 3 3 19 2010:369 1970 4 1 2 3 4 1 2 4 3 2010 11 1926 2011a:80 2011a:81 2008:27 2001a:82 2001a:85 2001a:82-3 2001a:95 2001b:32-3 2001b:32 2001b:33 2009:65 2005 2005:3 2005:4 2005:8 2005:8

More information

!!! 10 1 110 88 7 9 91 79 81 82 87 6 5 90 83 75 77 12 80 8 11 89 84 76 78 85 86 4 2 32 64 10 44 13 17 94 34 33 107 96 14 105 16 97 99 100 106 103 98 63 at 29, 66 at 58 12 16 17 25 56

More information

24.15章.微分方程式

24.15章.微分方程式 m d y dt = F m d y = mg dt V y = dy dt d y dt = d dy dt dt = dv y dt dv y dt = g dv y dt = g dt dt dv y = g dt V y ( t) = gt + C V y ( ) = V y ( ) = C = V y t ( ) = gt V y ( t) = dy dt = gt dy = g t dt

More information

極地研 no174.indd

極地研 no174.indd C O N T E N T S 02 10 13 no.174 June.2005 TOPICS06 1 45 46 3 12 4546 47 14 10 15 15 16 NEWS no.174 june.2005 0 100 200 300 400 500 600 700 100 100 Diameter,nm 10 10 45 20042 Feb Mar Apr May Jun Jul Aug

More information

Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestim

Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestim TS001 Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestimation 49 mwp-055 corrgram/ac/pac 56 mwp-009 dfgls

More information

76_01ver3.p65

76_01ver3.p65 GLOCOM Review 8:4 (76-1) 2003 Center for Global Communications 1 2 GLOCOM Review 8:4 (76-1) Recent research has been providing more support for the idea that Internet layers are formed along Power Law

More information

夏目小兵衛直克

夏目小兵衛直克 39(1906)1222 14(1817) 3(1832)1514(1843) 2628 6 (1853) (1854)3727 3(1856) 1 / 13 5(1858)6(1859) 5(1853) () () () () () () 3(1867)29 504111( 2 / 13 )98 23 18 2(1869)310283 100 50() 58 226 3313200982 5033

More information

nenkin.PDF

nenkin.PDF 1 31 1 WEB 10 3,544 429 13 10 22 11 7 WEB 1 2 41.0 15 80.0 20 46.7% 1000 55.8 1000 34.4 21 18.2 1000 23 25 41.0 49.2 29 90.6 42.7 33 56.4% 79.2% 67.4 51.7 37 39 83.7 1 91.0 93.6 9 2 3 1000 96.3 300 1000

More information

( )

( ) Web Web 1 3 1 21 11 22 23 24 3 2 3 4 5 1 1 11 22 9 2 3 15 11 22 2 11 21 4 5 ( ) 102 ( ) 1 ( 1 2001 Web 1 5 4 1 1 - 7 - [] - 7 10 11 12 12 1 10 1 12 - [] 1 1 2 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 3 1 47

More information