Download as PDF, PPTX
![PageRank via
PageRank details
PageRank by Google 3
3
2 5 The Model 0 0 0 3
2
1/ 6 1/ 2 0
2 5 6 1/ 6 0 0 1/ 3 0 0 7
1. follow edges uniformlyPwith
j 0
! 6 probability1/ 3, 0 0 7 eT P=eT
1/ 6 1/ 2 0 0 0
4
4 4 1/ 6 0 1/ 2 0 and 5
1/ 6 0 1/ 2 1/ 3 0 1
2. randomly jump 0
1/ 6 0 0 0 1 with probability
1 6 | {z }
1 6 1 , we’ll assume everywhere
P
equally likely
T 0
“jump” ! v = [ 1 ... 1 ]
n n eT v=1
î ó
Markov chain P + (1 )ve T x=x
The places we find the
unique x ) j 0, eT x = 1. are im-
surfer most often
Linear system ( portant pages.
P)x = (1 )v
Ignored dangling nodes patched back to v
9/40
algorithms later
David F. Gleich (Sandia)
David F. Gleich (Sandia) PageRank intro PageRank intro Purdue 6 / Purdue
36
UTRC Seminar
David Gleich, Purdue](https://image.slidesharecdn.com/gleich-pagerankanduq-120116101949-phpapp02/85/PageRank-9-320.jpg)
![What about me?
Multiple surfers should have an impact!
Each person picks from distribution A
...
# #
x(E [A]) E [x(A)]
& .
17/40
x(E [A]) 6= E [x(A)]
David F. Gleich (Sandia) Random sensitivity Purdue 15 / 36
UTRC Seminar
David Gleich, Purdue](https://image.slidesharecdn.com/gleich-pagerankanduq-120116101949-phpapp02/85/PageRank-17-320.jpg)
![alpha PageRank PageRa
RandomPageRank
dom alpha alpha
Random alpha PageRank
RAPr
or PageRank meets UQ
s the random variables as the random variables
Model PageRank
ageRank as the random variables
x(A) x(A)
x(A)
and look at
k E [x(A)] and Std [x(A)] .
at
E [x(A)] and Std [x(A)] .
E [x(A)] and Std [x(A)] .
18/40
Explored in Constantine and Gleich, WAW2007; and "
Constantine and Gleich, J. Internet Mathematics 2011.
UTRC Seminar
David Gleich, Purdue](https://image.slidesharecdn.com/gleich-pagerankanduq-120116101949-phpapp02/85/PageRank-18-320.jpg)
![A theoretical concern
Just one a problem
isn’t really
second ...
Z 1 Z 1
1
E [x( )] = x( ) ( ) d = (1 )( P) v ( )d
0 0
= 1 ( P) 1
!
P stochastic singular?
Yes, but ...
1
lim (1 )( P) v=x is unique
!1
22/40
(Think about P = 1, use Jordan Form of P to generalize)
UTRC Seminar
David Gleich, Purdue](https://image.slidesharecdn.com/gleich-pagerankanduq-120116101949-phpapp02/85/PageRank-22-320.jpg)
![Many PageRank properties are
What changes?
unchanged by a random alpha
Really, what stays the same!
x(A) A ⇠ Bet ( , b, , r) with 0 < r 1
1. E [ (A)] 0 and kE [x(A)]k = 1;
thus E [x(A)] is a probability distribution.
P î ó
2. E [x(A)] = =0
E A A +1 P v;
thus we can interpret E [x(A)] in length- paths.
3. for page with no in-links, (A) = (1 A) ;
thus E [ (A)] = (E [A]) and Std [ (A)] = Std [A]
23/40
But is this one useful?
UTRC Seminar
David Gleich, Purdue](https://image.slidesharecdn.com/gleich-pagerankanduq-120116101949-phpapp02/85/PageRank-23-320.jpg)
![Wikipedia test case (take 2)
RAPr on Wikipedia
RAPr on Wikipedia
EE [x(A)]
[x(A)] Std [x(A)]
Std [x(A)]
United States
United States United States
United States
C:Living people
C:Living people C:Living people
C:Living people
France
France C:Main topic classif.
C:Main topic classif.
United Kingdom
United Kingdom C:Contents
C:Contents
Germany
Germany C:Ctgs. by country
C:Ctgs. by country
England
England United Kingdom
United Kingdom
Canada
Canada France
France
Japan
Japan C:Fundamental
C:Fundamental
Poland
Poland England
England
24/40
Australia
Australia C:Ctgs. by topic
C:Ctgs. by topic
Note A A ⇠ Bet(0.5, 1.5, [0, 1]) ⇡ ⇡ empirical distribution on WikipediaGleich, Purdue
Note ⇠ Bet (0.5, 1.5, [0, 1]) empirical distribution Seminar
David
UTRC on Wikipedia](https://image.slidesharecdn.com/gleich-pagerankanduq-120116101949-phpapp02/85/PageRank-24-320.jpg)
![Ulam Networks
Ulam Networks
Ulam Networks
PageRank on a
dynamical system Networks yt+1
Chirikov map
Chirikov map Ulam networ
yt+1 = yt +k sin( t + t ) 1. divide phas
Ulam Ulam network Ulam t+1 = t +
network 2. form P base
hirikov map
Chirikov map
= Chirikov
+k sin( t
Ulam phase Ulam Networks
yt+1 = ytyt illustrates map1.1. divide network
space into uniform c
nicely +k sin(t + +t ) t ) divide phase space into uniform cel
Ulam network
+1 = = t Ulam Networks based ontrajectories.
the uncertainty.
NetworksP based onUlam network
+Ulam + yt+1 2.2. formmap
+1
yt+1 = yt +k sin( t + t ) 1. divide phase space into uniform cells
t+1 y yt+1
t +t+1 = t
t+1
ChirikovP P
form form
2. based on trajectories.
trajectories.
Chirikov map
Chirikov map yt+1 = yt +k sin( t + t ) 1. divide phase space
Ulam network
Ulam network
1. = t + yt+1
t t ) divide phase space into form P based
yt+1t+1 = t +k+k sin(+ t +)t+1 1. divide phase space into uniform cells on tr
y = y yt sin( t 2. uniform cells
t+1 = = +t yt+1
t+1 t + yt+1 form P P based trajectories.
2. 2. form based onon trajectories.
log(E [x(A)]) log(
log(E [x(A)]) log(Std [x(A)]))/ log(E Bet (2, 1
A ⇠ [x(A)])
Note Bet (2, 16)
A ⇠ White is larger, black is smaller
Note White is larger, black is smaller Google matrix, dynamical attractors, and
Google matrix, dynamical attractors, and Ulam networks, Shepelyansky and Zhirov, arXiv
David F. Gleich (Sandia) Random sensitivity
log(E [x(A)])
log(E [x(A)])
log(E [x(A)])
log(E [x(A)]) log(Std [x(A)]))/ log(E [x(A)]) [x(A)
log(Std[x(A)]))/ log(E [x(A)]) [x(
log(Std [x(A)]))/ log(E 23 [x(
log(Std log(E
25/40
David F. Gleich (Sandia) log(E [x(A)]) [x(A)]))/ log(Std/ 36
Random sensitivity Purdue
White is larger, black is smaller
⇠ Bet (2, 16)
A A ⇠ Bet (2, 16)
Note White is larger, black is is
Note White is larger, black
Bet (2, 16) A ⇠ Bet (2, 16)
Model from Shepelyasky and Zhirov, Bet(2, 16)
Asmaller "
Asmaller
⇠⇠
Phy. Rev. E. 2011.
Google matrix, dynamical attractors, andUTRCnetworks,smaller Gleich, Purdue
arXiv
Ulam Seminar
David
GoogleNote dynamical attractors, andblack is Shepelyansky and and Zhirov,
matrix, White is larger, Ulam networks, Shepelyansky Zhirov, arXiv](https://image.slidesharecdn.com/gleich-pagerankanduq-120116101949-phpapp02/85/PageRank-25-320.jpg)
![Convergence
0
10
Algorithms & "
Convergence
−5
10
Monte Carlo
−10
10
1. Monte Carlo
E [x(A)] −15
1 PN
10
⇡ N =1 x( ⇠A
0 1 2 3 4 5
) 0
10 10 10 10 10 10
10
2. Path Damping
E [x(A)] 10
−5
PN î ó
⇡ =0 E A A +1 P v
Path Damping
−10
10
3. Quadrature
E [x(A)] 10
−15 (No Std)
Rr 10
0 1
10
2
10 10
3
⇡ x( ) d ( ) 0
10
PN C
⇡ =1 x( ) −5
s
10
(h
Convergence toto semi-exact
Convergence semi-exact
solutions on a 335-nodestrong
solution on a 335-node graph −10
10 Quadrature
component.
(harvard500 strong component).
26/40
Blue = Beta(2,16)
16)
Blue Bet (2, −15
10
Green = Beta(1,1,0.1,0.9)
0.9)
Green Bet (1, 1, 0.1, 0 10 20 30 40 50 60 70 80 90 100
Salmon = uniform (0.6, 0.9)
Salmon Uniform(0.6,0.9)
David F. Gleich (Sandia) Random sensitivity
Red = Beta(-0.5, -0.5, 0.2, 0.7)
Red Bet ( 0.5, 0.5, 0.2, 0.7) UTRC Seminar
David Gleich, Purdue](https://image.slidesharecdn.com/gleich-pagerankanduq-120116101949-phpapp02/85/PageRank-26-320.jpg)
![Random alpha PageRank
has a rigorous convergence
Convergence
theory.
theory
Method Conv. Work Required What is N?
1 number of
Monte Carlo p N PageRank systems
N samples from A
Path Damping
r N+2 N + 1 matrix vector terms of
(without
N1+ products Neumann series
Std [x(A)])
number of
Gaussian
r 2N N PageRank systems quadrature
Quadrature
points
and r are parameters from Bet ( , b, , r)
28/40
David F. Gleich (Sandia) Random sensitivity UTRC Seminar
David Gleich, Purdue 27 / 36
Purdue](https://image.slidesharecdn.com/gleich-pagerankanduq-120116101949-phpapp02/85/PageRank-28-320.jpg)
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- 12. Richardson is arobust, simple algorithm to compute PageRank Given α, P, v (I ↵P)x = (1 ↵)v Richardson ) (k+1) (k) x = ↵Px + (1 ↵)v (k) k error = kx xk1 2↵ 12/40 UTRC Seminar David Gleich, Purdue
- 13. Sensitivity 13/40 UTRC Seminar David Gleich, Purdue
- 14. Which sensitivity? PageRankcirca 2006 ( P)x = (1 )v Sensitivity to the links : examined and understood Sensitivity to the jump : examined, understood, and useful Sensitivity to : less well understood 14/40 For information about how to compute the PageRank derivative, see: Gleich, Glynn, Golub, Greif. Three results on the PageRank vector, 2007. UTRC Seminar David Gleich, Purdue
- 15. Wikipedia test case PageRank on Wikipedia = 0.50 = 0.85 = 0.99 United States United States C:Contents C:Living people C:Main topic classif. C:Main topic classif. France C:Contents C:Fundamental Germany C:Living people United States England C:Ctgs. by country C:Wikipedia admin. United Kingdom United Kingdom P:List of portals Canada C:Fundamental P:Contents/Portals Japan C:Ctgs. by topic C:Portals Poland C:Wikipedia admin. C:Society Australia France C:Ctgs. by topic Note Top 10 articles on Wikipedia with highest PageRank 15/40 David F. Gleich (Sandia) Sensitivity Purdue 11 / 36 UTRC Seminar David Gleich, Purdue
- 16. What is alpha? Whatis alpha? The teleportation parameter! Author Brin and Page (1998) 0.85 Najork et al. (2007) 0.85 Litvak et al. (2006) 0.5 Experiment (slide 19) 0.63 Algorithms (...) 0.85 For you,αis clear. or you, is clear oogle Google wants PageRank for everyone wants PageRank for everyone 16/40 UTRC Seminar David Gleich, Purdue
- 17. What about me? Multiplesurfers should have an impact! Each person picks from distribution A ... # # x(E [A]) E [x(A)] & . 17/40 x(E [A]) 6= E [x(A)] David F. Gleich (Sandia) Random sensitivity Purdue 15 / 36 UTRC Seminar David Gleich, Purdue
- 18. alpha PageRank PageRa RandomPageRank dom alpha alpha Random alpha PageRank RAPr or PageRank meets UQ s the random variables as the random variables Model PageRank ageRank as the random variables x(A) x(A) x(A) and look at k E [x(A)] and Std [x(A)] . at E [x(A)] and Std [x(A)] . E [x(A)] and Std [x(A)] . 18/40 Explored in Constantine and Gleich, WAW2007; and " Constantine and Gleich, J. Internet Mathematics 2011. UTRC Seminar David Gleich, Purdue
- 19. Alpha, measured fromusers! What is alpha based on users? 3.0 InfBeta( 3.2 , 2.0 , 1.9e−05 , 0.0019 ) mean 0.63 2.5 mode 0.69 2.0 density 1.5 1.0 0.5 0.0 0.0 0.2 0.4 0.6 0.8 1.0 Raw α 19/40 see Gleich et al. WWW2010 for more Constantine, Flaxman, Gleich, Gunawardana, Tracking the Random Surfer, WWW2010. UTRC Seminar David Gleich, Purdue
- 20. What is A? A simple model for alpha 20/40 Bet ( , b, , r) UTRC Seminar David Gleich, Purdue
- 21. An Examplerandom variables The PageRank x 1 3 x 2 2 5 x 3 4 x4 1 6 x 5 x 6 21/40 0 0.5 UTRC Seminar David Gleich, Purdue
- 22. A theoretical concern Justone a problem isn’t really second ... Z 1 Z 1 1 E [x( )] = x( ) ( ) d = (1 )( P) v ( )d 0 0 = 1 ( P) 1 ! P stochastic singular? Yes, but ... 1 lim (1 )( P) v=x is unique !1 22/40 (Think about P = 1, use Jordan Form of P to generalize) UTRC Seminar David Gleich, Purdue
- 23. Many PageRank propertiesare What changes? unchanged by a random alpha Really, what stays the same! x(A) A ⇠ Bet ( , b, , r) with 0 < r 1 1. E [ (A)] 0 and kE [x(A)]k = 1; thus E [x(A)] is a probability distribution. P î ó 2. E [x(A)] = =0 E A A +1 P v; thus we can interpret E [x(A)] in length- paths. 3. for page with no in-links, (A) = (1 A) ; thus E [ (A)] = (E [A]) and Std [ (A)] = Std [A] 23/40 But is this one useful? UTRC Seminar David Gleich, Purdue
- 24. Wikipedia test case(take 2) RAPr on Wikipedia RAPr on Wikipedia EE [x(A)] [x(A)] Std [x(A)] Std [x(A)] United States United States United States United States C:Living people C:Living people C:Living people C:Living people France France C:Main topic classif. C:Main topic classif. United Kingdom United Kingdom C:Contents C:Contents Germany Germany C:Ctgs. by country C:Ctgs. by country England England United Kingdom United Kingdom Canada Canada France France Japan Japan C:Fundamental C:Fundamental Poland Poland England England 24/40 Australia Australia C:Ctgs. by topic C:Ctgs. by topic Note A A ⇠ Bet(0.5, 1.5, [0, 1]) ⇡ ⇡ empirical distribution on WikipediaGleich, Purdue Note ⇠ Bet (0.5, 1.5, [0, 1]) empirical distribution Seminar David UTRC on Wikipedia
- 25. Ulam Networks Ulam Networks UlamNetworks PageRank on a dynamical system Networks yt+1 Chirikov map Chirikov map Ulam networ yt+1 = yt +k sin( t + t ) 1. divide phas Ulam Ulam network Ulam t+1 = t + network 2. form P base hirikov map Chirikov map = Chirikov +k sin( t Ulam phase Ulam Networks yt+1 = ytyt illustrates map1.1. divide network space into uniform c nicely +k sin(t + +t ) t ) divide phase space into uniform cel Ulam network +1 = = t Ulam Networks based ontrajectories. the uncertainty. NetworksP based onUlam network +Ulam + yt+1 2.2. formmap +1 yt+1 = yt +k sin( t + t ) 1. divide phase space into uniform cells t+1 y yt+1 t +t+1 = t t+1 ChirikovP P form form 2. based on trajectories. trajectories. Chirikov map Chirikov map yt+1 = yt +k sin( t + t ) 1. divide phase space Ulam network Ulam network 1. = t + yt+1 t t ) divide phase space into form P based yt+1t+1 = t +k+k sin(+ t +)t+1 1. divide phase space into uniform cells on tr y = y yt sin( t 2. uniform cells t+1 = = +t yt+1 t+1 t + yt+1 form P P based trajectories. 2. 2. form based onon trajectories. log(E [x(A)]) log( log(E [x(A)]) log(Std [x(A)]))/ log(E Bet (2, 1 A ⇠ [x(A)]) Note Bet (2, 16) A ⇠ White is larger, black is smaller Note White is larger, black is smaller Google matrix, dynamical attractors, and Google matrix, dynamical attractors, and Ulam networks, Shepelyansky and Zhirov, arXiv David F. Gleich (Sandia) Random sensitivity log(E [x(A)]) log(E [x(A)]) log(E [x(A)]) log(E [x(A)]) log(Std [x(A)]))/ log(E [x(A)]) [x(A) log(Std[x(A)]))/ log(E [x(A)]) [x( log(Std [x(A)]))/ log(E 23 [x( log(Std log(E 25/40 David F. Gleich (Sandia) log(E [x(A)]) [x(A)]))/ log(Std/ 36 Random sensitivity Purdue White is larger, black is smaller ⇠ Bet (2, 16) A A ⇠ Bet (2, 16) Note White is larger, black is is Note White is larger, black Bet (2, 16) A ⇠ Bet (2, 16) Model from Shepelyasky and Zhirov, Bet(2, 16) Asmaller " Asmaller ⇠⇠ Phy. Rev. E. 2011. Google matrix, dynamical attractors, andUTRCnetworks,smaller Gleich, Purdue arXiv Ulam Seminar David GoogleNote dynamical attractors, andblack is Shepelyansky and and Zhirov, matrix, White is larger, Ulam networks, Shepelyansky Zhirov, arXiv
- 26. Convergence 0 10 Algorithms & " Convergence −5 10 Monte Carlo −10 10 1. Monte Carlo E [x(A)] −15 1 PN 10 ⇡ N =1 x( ⇠A 0 1 2 3 4 5 ) 0 10 10 10 10 10 10 10 2. Path Damping E [x(A)] 10 −5 PN î ó ⇡ =0 E A A +1 P v Path Damping −10 10 3. Quadrature E [x(A)] 10 −15 (No Std) Rr 10 0 1 10 2 10 10 3 ⇡ x( ) d ( ) 0 10 PN C ⇡ =1 x( ) −5 s 10 (h Convergence toto semi-exact Convergence semi-exact solutions on a 335-nodestrong solution on a 335-node graph −10 10 Quadrature component. (harvard500 strong component). 26/40 Blue = Beta(2,16) 16) Blue Bet (2, −15 10 Green = Beta(1,1,0.1,0.9) 0.9) Green Bet (1, 1, 0.1, 0 10 20 30 40 50 60 70 80 90 100 Salmon = uniform (0.6, 0.9) Salmon Uniform(0.6,0.9) David F. Gleich (Sandia) Random sensitivity Red = Beta(-0.5, -0.5, 0.2, 0.7) Red Bet ( 0.5, 0.5, 0.2, 0.7) UTRC Seminar David Gleich, Purdue
- 27. f(α) ⋅ g(α) . ⋅ f (α) = 1724683103168320512000α 102 − 351689859974563275916800α 101 + 1046657678560756011923040α 100 (α) = 21252680112847680000α 102 +332821515558986503317268308α 99 + 202994690094545539249274953458α 98 + 701216550622104187641429941160α 97 −3542775096896042918400α 101 − 377301357230918051819160α 100 + 62030166204003769204027938α 99 + 301903572553392042618587937α 98 +38942435173273232195508862504752α 96 − 5204876256969489587508598423780757α 95 − 53419116345848724180375395029139614α 94 −27515144995670593102754792187α 97 − 1391342388530090922919905979557α 96 − 11397010225845179645798293856049α 95 +1621997105501543781796265745838677670α + 17992097277595516775992937444966323725α 92 93 +487046819801240647260974920877667α 94 + 8641748415645906110710596472701695α 93 − 14615573868254463557271968794871527α 92 −228388738389199148614341585444680228464α 91 − 2572935401339464873388154472765864295466α 90 −1455304405730842808585234463006780870α 91 − 16140532952116322684344866986683755014α 90 −18662047188535851000868073690251020472621α 89 − 155192964832717622674637679380949267008397α 88 −107685923577790689207116358432796101348α 89 + 3574857500140390342079726927167132783327α 88 +13633798075806927018912795365187923947976816α 87 + 153692481592717017931843564092779914769739855α 86 +76245995916566900197088870723441134067760α 87 − 320477613697118756563592647774688786780579α 86 −2424702525231324896856434133527720085459106818α 85 − 34112664906875644324640001664890877920583430935α 84 −14315018719450474212530996756919665488506623α 85 − 12271042346558183829899943919127664848771235α 84 +222921632950502905446093540571509314548545319158α 83 + 4458381340774458139955262362762709170337141183042α 82 +1538719934896052457300693234469902122130588440α 83 + 7259823837632938466306787148779956756499503259α 82 −9722398912749159172830586061232227612575398195577α 81 − 402863595222192101330043246404750577170418624210463α 80 −91383277962053778179963631846131934198363974003α 81 − 912158632690159715631486922494993985581191177254α 80 −241296146875962767748365749082981265577900593669099α 79 + 26884891161116233003550134767867058390000240645389885α 78 +1124589169570249225316595386438810701468062018941α 79 − 55599491760340084897708205765116975153096053881206α 78 +75002935639704657680175868562515328344632861061620026α 77 − 1355245718493528694128677343628002432897202221776993666α 76 +254197028878341726795811304127085084201803714274594α 77 − 1155102780712932745491921904562487673324953687625090α 76 −6666337432948865424681896342751813538288258918631143898α 75 + 50876562123828411130342908134923596879946044492587906688α 74 −19623309116424352882311523132748440745863270150867432α 75 − 72367264828688457023192884699324797029606326773402260α 74 +385972738637461890892793659070699381929652086327544953064α 73 − 1324370012053495348856190918458325441254102678707139546912α 72 +510591330662979105902331311824358111451756310585317896α 73 + 6560635654785580651459993551515346226540950556472012168α 72 −16416792980158036153780188009203628703318521649963318398744α 71 + 17510197624369310054645143199845105805941154913191274775360α 70 +11841946546859350197679256661965428675545845230913012752α 71 − 222422692257166102165445803087102201095333519552710152624α 70 +533320137070985354296793454864336229974212018883255863520736α 69 + 275502212308122569075672900514808641788656066608417565862128α 68 −1447290325427425453794609658098719385231428839474861685840α 69 + 2125011726240928873652963898522501443619028980101705108896α 68 −13429082722840051523544458153489421210623008268881676515202688α 67 +56163879158282775333105949842095267377034088228166264755488α 67 + 133653341840138472687713523321901358136789047544268798190144α 66 −23110058843365910555627839838104471746030299594537756688223008α 66 −1165851790876533575106055126719543401792990924852555883239232α 65 +262081257818502675810469542460738736851208401216965512926700160α 65 −7205045167922126127366881708591461911830986630512778219907200α 64 +729407390179003876249104385055674850942454472967192021090685376α 64 +8196149623293434725419276185048399130126199483584663609965696α 63 −3847937179452929633833233710422322341537775007885518269634539392α 63 +190347290617372900092754118891814664663338859287254054095265536α 62 −15488141989129507247130473020571135237573107436265881323677072000α 62 +296403177926940870392191966640325276665391672647048523475737600α 61 +36050325771659567239591241663693950811960305821938730156334667776α 61 −3179986962227253427695124755087565566711837258936975824737021952α 60 +246707867322513330007744656494007568641366676837744833157870986240α 60 −12273950891286672757637149571293897139589064857886165164957404160α 59 +66698815198854350338382524697115939758820557665663603703007667712α 59 +31408962973625270006925545397999409094566386715881351869322999808α 58 −2959446110396107328472639479854607457433633185566140760490226286592α 58 +253177395609699067378776631302481890469651122338031051366108686336α 57 −12528512804728910558071029225789548204605758683928995029146000314368α 57 −15354832074031738521204442047058295183786064138590507845987942400α 56 +19985525277247932558760938212461479524515746377831707793868714172416α 56 −3457076532174502560822426326142749948730584183953208907119801098240α 55 +343866190600408921247069416527135879796528858737524668958998645633024α 55 −6661437625275114934838338879511817915494254490727882100057772130304α 54 +237159992339459130849980507259488489676582642639199883151854812422144α 54 +28704083600179676384022705580143799967745682382583318411010759639040α 53 −6150352682504179603648657901968989091083378789857325448622418220859392α 53 +173119877625293135511416194747967318688771201702803231109775079243776α 52 −12507084588874068660420542622454441021005365876210831205762085535989760α 52 +42285615967170654345485778244291908234053330314299949447131636826112α 51 +76052343558405304817491728967709919562879906814237879556140479278219264α 51 −3092545165791022831669116892040565590342926023532342815170675350831104α 50 +281657470545819893901842735393494111347269819443029672934492155921629184α 50 −7385454932946443098573906964601689327710122151758555775183630113177600α 49 −524010169549932716315240835391286383538294517356494888193446880264060928α 49 +44090325705050939960465955316629060665099648652920301218388343039721472α 48 −4283228548253488673520351046009849054273946705738400536855052450584985600α 48 −2155194129185085332436034710334032595487897368550943059587873095183237120α 47 +180430494757250498411208705426475191214202221095549279916495110854934528α 47 +44942983365390912258646063248936155917171235534162037124027584790839951360α 46 −493525709032718650057526281767644848135900953167613963100373354560880640α 46 +123764976043225311633569878034493895722302903722502785220748272524591104000α 45 −3036843091999605016958964058463815080108229170215714733277797291506270208α 45 −263604612819883334094471942440378055857630908721587551326277602165812887552α 44 +3865732160987803528525842299699004166440912343665407865787648656852123648α 44 −2043045823645899057845901056050369454115577248500633141166053687383937777664α 43 +40165478772124194334610082404062794103423683134161618111009172215000203264α 43 −883572534249006235663814128436259426227447113734226469390794110452279279616α 42 −11270446090439842262616868429380066718469755470664378191173671836048162816α 42 +22029266389692672474905374638580604237511322238870051881693348503640495620096α 41 −431725269187383778295706776607285692623377582173153891079752971949306806272α 41 +45203159614332573226167349621344476004471313288020398240113991699259941978112α 40 −223578855128847742913688810087057318022143978462109332025481258567127269376α 40 −168198634626680009003513480377236264968641977685259854545270514440488513175552α 39 +3806641102807223385639875513891980988734164017656312910101180605432770592768α 39 −668594708420193863217346925249650551196858552245852383052928679191604052885504α 38 +4698338022493830197418469777664958098209079184719648168122484318843776794624α 38 +829995196451920004299651167659513171123326408698056871202815263749436350660608α 37 −27472779560617412642244986656083233718762546534015558981997009063520073940992α 37 +6805400890411122172338081288981379379115027947251954438964848554500327026458624α 36 −53681346508826005770227174053581590059283954164048929404839105532796000534528α 36 −839859147076619012613401783607878586283917926703478867476334483102478263910400α 35 +159792483519832871643195761447614587325418857137220582772566606510963040452608α 35 −54336251411672379109173054554388944990018972031681985156883655345205770838867968α 34 +447775073289651418862702364745936934030540232799739862181009845955145918054400α 34 −31763834543511199735483407052389951464492348704450435677017768682913434678853632α 33 −716151822637851063198942928932119452580573299424788537816341142171636199325696α 33 +357712343186400835247921272739995225258056636329416844164038875886993432486346752α 32 −2933014614963404405624949533910517712184375976693976408790612422895031925342208α 32 +394894109850616441422196163643656479874423531345017994904270039571808903743143936α 31 +2123830137329614973540541687269913350581043300869459472923500012177964595675136α 31 −1993929054800515710688917066299914269693286626662952457319746685784090804001701888α 30 +15491595398748844916213727820453788960246908641990943232584972825253134896988160α 30 −3002267549064744794430368624087097289757148076091004127530245571997364275264880640α 29 −567958048418299255333286711763252835749000069031930133372386182151207554908160α 29 +9573037450950832796546125489519791559144293205440801001596502044790259906531819520α 28 −66470511672905973490254270449160748571544305482918584099892594998203682442444800α 28 +17344649689902103638748302705765490194768583990372876266091126135709005379492904960α 27 −41709961606955286961348486645761651227147583272088758133872408100389592005345280α 27 −40109860118705371377719161262775470420310263138301806878152530252877875499258347520α 26 +230054579604523153712298391601390663928014143964616089795553744517711724229427200α 26 −81164940713776050502710413692301000793918577563223455903690236298808582388129464320α 25 +329047428589773383037144315393721888182438735406281384979987048470391313714380800α 25 +148564684652598057008901304730992665142722743799406464890491019151228896289384038400α 24 −624457510685469088854461981456149137717339107570818384916469113052631000311398400α 24 +316011966716392521139260824696069224379619965509016982919437611079336611648687308800α 23 −1677023335298418194342571458068169568589073430891247365956379137661470030954496000α 23 −493937443242584182232311411058386151572960694882377097092991520649901348015308800000α 22 +1230550173656441248007569837874753909716280107131735470279305802166985335767040000α 22 −1032631097012698995004666052463769745257461602028357530776684844670222403998056448000α 21 +6678146820080249693682249156290720809288474225484848277349949390581823092817920000α 21 +1496051498205595023212876520305710378404801491740260076675413316113755884612485120000α 20 −1335521590342284671869409797110636836566621705504540007316206486982151246970880000α 20 +2808040259722605050478570986966436499493340536522637266197921902172159568196403200000α 19 −22059887560957847625176129319162020059098234073297049383363906396128975739944960000α 19 −4136197022520781923456607241837348242573554483478641216258357714108027813809356800000α 18 −785340364420012115414030768139171427530706940353196376588668778473179840512000000α 18 −6160485939298474432256897143548698073388765535732087612799636625216399725507379200000α 17 +61717145472396641090916430698897769773248344842243911905796114382139890755174400000α 17 +10181878863815582516096533223816217477281300683613861533575784672221983047837286400000α 16 +4618799817652795890614174914969648296550799276151584724886711273496204279808000000α 16 +10194856369622478439949806821168034096091795201083524149176484646680561362088755200000α 15 −145443881953486865648263190807202565800657985019098154597005260614892420857856000000α 15 −21187043154586589769777874169878395124445179056372063547781637907554829911195648000000α 14 +3895092842622840658053685865827455168729291218619441351061760037262624555008000000α 14 −10797617499303349106653965603456976243130791900284770633819420554804763717271552000000α 13 +280377685657177839855779204679112256388859412881172644774038185688216297799680000000α 13 +34810029936836090031365778846622873044044684351940766602929819682347633942200320000000α 12 −61886949354628165807560200683179015577169820467161436162087652305392411607040000000α 12 +2444726911101623695480273766948648572307801537702233726799335232903066482114560000000α 11 −419675757995547385956754793581818014152422427747599875638509945701343323750400000000α 11 −41556351242381300546605413427086996396993985304666948472681225427743791067955200000000α 10 +198444685626856286689595946806119633184708804987305884557282973568786130534400000000α 10 +13235190618796698664164720739564065289559223880035423917829189953498612603289600000000α 9 +448747751865602411338231508161295374031102511536034615950378207124016594944000000000α 9 +31707117886734781934293206235313206269589256456457305316683904435212337020928000000000α 8 −354225411849996408405676297836399354389793212596699228226946778751972671488000000000α 8 −22902862215982163769314078339007120966645769612414912118902053217891120054272000000000α 7 −289553838601814478908147100882111896550771868124112559407400778696805580800000000000α 7 −10861447231493527964796381160797577847506774386629058766245206345294177894400000000000α 6 +380193432519284724415876033554186663453423948344477630293719517144232755200000000000α 6 +49868638731749836953497035941697409493586060953068752243112234044096512000000000000α 5 27/40 +16702340614440996726321322519580478377784196762456013465355368892183714201600000000000α 5 −2484655700299390942962097170834933290427413703835951243538833117682860032000000000000α 4 −225214852583720088017543526212238701302651117601148886021831714815344640000000000000α 4 −4413329047578208225715144832646023361841607400402542869168917500552806400000000000000α 3 +65704820370519415064487362188463863760063365628098565999947778359296000000000000000α 3 +2487780731058996453939104246064539866264498778933228932687035282279628800000000000000α 2 +49648864534173955171275387887713942931184684832027306458656054181888000000000000000α 2 −402148158541143771038030692426712820265062425103540831235367384383488000000000000000α −35756856984770583727093678769849105127720172150476292008503798661120000000000000000α −5203808713264169193283107063136995887025759130647063545708229427200000000000000000 +6649311133615327302528414580675050300088470000271247863960515379200000000000000000 Figure 2.5 – A PageRank function. x 1 (α) = (−236030) f (α)(α), see section . x1(α) = -23/6030 f(α)/g(α) Figure 2.5 (continued). UTRC Seminar David Gleich, Purdue
- 28. Random alpha PageRank hasa rigorous convergence Convergence theory. theory Method Conv. Work Required What is N? 1 number of Monte Carlo p N PageRank systems N samples from A Path Damping r N+2 N + 1 matrix vector terms of (without N1+ products Neumann series Std [x(A)]) number of Gaussian r 2N N PageRank systems quadrature Quadrature points and r are parameters from Bet ( , b, , r) 28/40 David F. Gleich (Sandia) Random sensitivity UTRC Seminar David Gleich, Purdue 27 / 36 Purdue
- 29. Convergence of quadraturein the r=1 regime is matrix dependent. Singularities 10 0.03 8 6 0.02 4 0.01 2 0 0 1.00129 2 3 −2 4 5 6 7 8 −0.01 −4 9 10 −6 −0.02 −8 −10 −0.03 −10 −5 0 5 10 0.97 0.98 0.99 1 1.01 1.02 1.03 29/40 log10(9+|1/λ|)eiarg(1/λ) 1/λ Note 500-node harvard500 graph from Cleve Moler, left plot is Gleich, Purdue UTRC Seminar David
- 30. Establishing this theoretical convergenceproved independently useful. Constantine, Gleich, and Iaccarino. Spectral Methods for Parameterized Matrix Equations, SIMAX, 2010. A(s)x(s) = b(s) , A(J 1 )x(J 1 ) = b(J 1 ) ) A(J N )x(J N ) = b(J N ) or ) AN (J 1 )xN (J 1 ) = bN (J 1 ) Constantine, Gleich, and Iaccarino. A factorization of the spectral Galerkin system for parameterized matrix equations: derivation and applications, SISC 2011. 30/40 How to compute the Galerkin solution in a weakly intrusive manner.! UTRC Seminar David Gleich, Purdue
- 31. A real test-case Webspam application Hosts of uk-2006 are labeled as spam, not-spam, other P R f FP FN Baseline 0.694 0.558 0.618 0.034 0.442 Beta(0.5,1.5) 0.695 0.561 0.621 0.034 0.439 Beta(1,1) 0.698 0.562 0.622 0.033 0.438 Beta(2,16) 0.699 0.562 0.623 0.033 0.438 31/40 Note Bagged (10) J48 decision tree classifier in Weka, mean of 50 repetitions from 10-fold cross-validation of 4948 non-spam and 674 spam hosts (5622 total). Becchetti et al. Link analysis for Web spam detection, 2008. David F. Gleich (Sandia) Random sensitivity UTRC Seminar David Gleich, Purdue Purdue 28 / 36
- 32. New directions 32/40 UTRC Seminar David Gleich, Purdue
- 33. Data driven surrogatefunctions Beyond spectral methods for UQ 33/40 UTRC Seminar David Gleich, Purdue
- 34. j r Square s ) t t A L B Network alignment 34/40 m ximize w T x + 1 xT Sx UTRC Seminar David Gleich, Purdue
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- 36. Local methods formassive FOR KATZ TOP-K ALGORITHM network analysis Approximate where is sparse Keep sparse too Ideally, don’t “touch” all of This is possible for personalized PageRank! 36/40 David F. Gleich (Purdue) Univ. Chicago SSCS Seminar 34 of 47 UTRC Seminar David Gleich, Purdue
- 37. Graph spectra Graph spectra 37/40 UTRC Seminar David Gleich, Purdue
- 38. What about time? Realnetworks evolve in time. What to do? Look towards dynamical systems! 38/40 UTRC Seminar David Gleich, Purdue
- 39. What about time? Realnetworks evolve in time. What to do? Look towards dynamical systems! Now I must be preaching to the choir! 39/40 UTRC Seminar David Gleich, Purdue
- 40. Questions? www.cs.purdue.edu/homes/dgleich Google “DavidGleich” 40 UTRC Seminar David Gleich, Purdue