SlideShare a Scribd company logo
1 of 69
Preserving Personalized
Pagerank in Subgraphs
(Andrea Vattani, Deepayan Chakrabarti, Maxim
                  Gurevich)




                                               1
a       a


    b


c       c
“Due to space
constraints, complete
 proofs of our claims
will appear in the full
version of the paper.”
p = αr + (1 − α)A D
                t     −1
                           p
p
α
r
A
D
r
r[i]
∞
                                          [t]
pi (j) =                α(1 −          α)pi (j)
                t=0                    t
    [t]                                        1
   pi (j)   =
                                             d+ (kl )
                k1 =i,k2 ,··· ,kt+1 =j l=1
G = (V, E)
   S⊂V
         p
         G


     pG[S]


         ˜
         p   G
˜
min d(p , p
       G   G[S]
                  )
|S| = o(n/ log n)

           1/2 − o(1)   Ω(|S|)
S ⊂S
                                              ∗
                                       u
       S
                                          ∗
S                                     u
                              ∗
                          u
wG (i, j) = 1
j∈V
G = (V, wG )
        S⊂V

H = (S ∪ SIN K, wH )
pi (j)
 G
         = pi (j)
            H

         ∀i, j ∈ S
a
a

        sampling a,c
    b                      SINK

        Remove b
c
                       c
∞
wH (x, y)+ = (1 − α)wG (x, z)wG (z, y)         [(1 − α)wG (z, z)]
                                                                t

                                         t=0
wH (x, SIN K)+ = wG (x, z) −                    wH (x, y)
                               y=z;wG (z,y)>0
P   H             pi
                   H
                       =   pi
                            G

    WH
            1
     t
    WH   =     (I − α(P ) )
                       H −1
           1−α

                O(E + |S| )     2
wH (u, v) < τ
wH (u, SIN K)+ = wH (u, v), wH (u, v) = 0
||P (j) − P (j)||1
     H     R
                         1−α
                    ≤ 2τ
      |S| + 1             α

P (j)
 H

P (j)
 R
v=            pj (i),
               G
                        i∈S
     j∈V S

r(j) = r(j ), j, j ∈ V  S


               pr (i), ∀i ∈ S
                   G
SINK




       Page
       Rank
1    1
wH   (SOU RCE, l) =                        pj
                                            G
                                                (k)wG (k.l), l ∈ S
                    |V  S| α
                                j,k∈V S




 wH (SOU RCE, SIN K) = 1 −                 wH (SOU RCE, i)
                                    i∈S
pr (i) = pr
  G           H
                  (i), ∀i ∈ S
         
          r(k),                k∈S
 r (k) =   ρ|V  S|,            k = SOU RCE
         
           0,                   k = SIN K
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)

More Related Content

What's hot

深層生成モデルを用いたマルチモーダルデータの半教師あり学習
深層生成モデルを用いたマルチモーダルデータの半教師あり学習深層生成モデルを用いたマルチモーダルデータの半教師あり学習
深層生成モデルを用いたマルチモーダルデータの半教師あり学習Masahiro Suzuki
 
【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...
【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...
【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...Deep Learning JP
 
M. Dimitrijević, Noncommutative models of gauge and gravity theories
M. Dimitrijević, Noncommutative models of gauge and gravity theoriesM. Dimitrijević, Noncommutative models of gauge and gravity theories
M. Dimitrijević, Noncommutative models of gauge and gravity theoriesSEENET-MTP
 
確率的推論と行動選択
確率的推論と行動選択確率的推論と行動選択
確率的推論と行動選択Masahiro Suzuki
 
Variational AutoEncoder
Variational AutoEncoderVariational AutoEncoder
Variational AutoEncoderKazuki Nitta
 
A note on the density of Gumbel-softmax
A note on the density of Gumbel-softmaxA note on the density of Gumbel-softmax
A note on the density of Gumbel-softmaxTomonari Masada
 
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein GradientsDeep Learning JP
 
Hiroaki Shiokawa
Hiroaki ShiokawaHiroaki Shiokawa
Hiroaki ShiokawaSuurist
 
Andreas Eberle
Andreas EberleAndreas Eberle
Andreas EberleBigMC
 
Laplace table
Laplace tableLaplace table
Laplace tablenoori734
 
[DL輪読会]近年のオフライン強化学習のまとめ —Offline Reinforcement Learning: Tutorial, Review, an...
[DL輪読会]近年のオフライン強化学習のまとめ —Offline Reinforcement Learning: Tutorial, Review, an...[DL輪読会]近年のオフライン強化学習のまとめ —Offline Reinforcement Learning: Tutorial, Review, an...
[DL輪読会]近年のオフライン強化学習のまとめ —Offline Reinforcement Learning: Tutorial, Review, an...Deep Learning JP
 
C Sanchez Reduction Saetas
C Sanchez Reduction SaetasC Sanchez Reduction Saetas
C Sanchez Reduction SaetasMiguel Morales
 
Dynamical t/U expansion for the doped Hubbard model
Dynamical t/U expansion for the doped Hubbard modelDynamical t/U expansion for the doped Hubbard model
Dynamical t/U expansion for the doped Hubbard modelWenxingDing1
 

What's hot (16)

深層生成モデルを用いたマルチモーダルデータの半教師あり学習
深層生成モデルを用いたマルチモーダルデータの半教師あり学習深層生成モデルを用いたマルチモーダルデータの半教師あり学習
深層生成モデルを用いたマルチモーダルデータの半教師あり学習
 
Ch13 16
Ch13 16Ch13 16
Ch13 16
 
【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...
【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...
【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...
 
M. Dimitrijević, Noncommutative models of gauge and gravity theories
M. Dimitrijević, Noncommutative models of gauge and gravity theoriesM. Dimitrijević, Noncommutative models of gauge and gravity theories
M. Dimitrijević, Noncommutative models of gauge and gravity theories
 
確率的推論と行動選択
確率的推論と行動選択確率的推論と行動選択
確率的推論と行動選択
 
Variational AutoEncoder
Variational AutoEncoderVariational AutoEncoder
Variational AutoEncoder
 
A note on the density of Gumbel-softmax
A note on the density of Gumbel-softmaxA note on the density of Gumbel-softmax
A note on the density of Gumbel-softmax
 
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
[DL輪読会]The Cramer Distance as a Solution to Biased Wasserstein Gradients
 
Hiroaki Shiokawa
Hiroaki ShiokawaHiroaki Shiokawa
Hiroaki Shiokawa
 
Andreas Eberle
Andreas EberleAndreas Eberle
Andreas Eberle
 
Laplace table
Laplace tableLaplace table
Laplace table
 
Semi vae memo (2)
Semi vae memo (2)Semi vae memo (2)
Semi vae memo (2)
 
[DL輪読会]近年のオフライン強化学習のまとめ —Offline Reinforcement Learning: Tutorial, Review, an...
[DL輪読会]近年のオフライン強化学習のまとめ —Offline Reinforcement Learning: Tutorial, Review, an...[DL輪読会]近年のオフライン強化学習のまとめ —Offline Reinforcement Learning: Tutorial, Review, an...
[DL輪読会]近年のオフライン強化学習のまとめ —Offline Reinforcement Learning: Tutorial, Review, an...
 
Semi vae memo (1)
Semi vae memo (1)Semi vae memo (1)
Semi vae memo (1)
 
C Sanchez Reduction Saetas
C Sanchez Reduction SaetasC Sanchez Reduction Saetas
C Sanchez Reduction Saetas
 
Dynamical t/U expansion for the doped Hubbard model
Dynamical t/U expansion for the doped Hubbard modelDynamical t/U expansion for the doped Hubbard model
Dynamical t/U expansion for the doped Hubbard model
 

Viewers also liked

Nonlinear latent factorization by embedding multiple user interests(Recsys 2013)
Nonlinear latent factorization by embedding multiple user interests(Recsys 2013)Nonlinear latent factorization by embedding multiple user interests(Recsys 2013)
Nonlinear latent factorization by embedding multiple user interests(Recsys 2013)ybenjo
 
R's anti sparseness
R's anti sparsenessR's anti sparseness
R's anti sparsenessybenjo
 
useR!2010 matome
useR!2010 matomeuseR!2010 matome
useR!2010 matomeybenjo
 
Topic Model Survey (wsdm2012)
Topic Model Survey (wsdm2012)Topic Model Survey (wsdm2012)
Topic Model Survey (wsdm2012)ybenjo
 
Overcoming browser cookie churn with clustering in wsdm2012 reading
Overcoming browser cookie churn with clustering in wsdm2012 readingOvercoming browser cookie churn with clustering in wsdm2012 reading
Overcoming browser cookie churn with clustering in wsdm2012 readingybenjo
 
AJACS17
AJACS17AJACS17
AJACS17ybenjo
 
AJACS HONGO8 (mining in DBCLS)
AJACS HONGO8 (mining in DBCLS)AJACS HONGO8 (mining in DBCLS)
AJACS HONGO8 (mining in DBCLS)ybenjo
 
Personalized next-song recommendation in online karaokes(Recsys 2013)
Personalized next-song recommendation in online karaokes(Recsys 2013)Personalized next-song recommendation in online karaokes(Recsys 2013)
Personalized next-song recommendation in online karaokes(Recsys 2013)ybenjo
 
patent analysis(LDA) and spotfire
patent analysis(LDA) and spotfirepatent analysis(LDA) and spotfire
patent analysis(LDA) and spotfireybenjo
 
とあるサイトの禁書目録(アクセスログ)
とあるサイトの禁書目録(アクセスログ)とあるサイトの禁書目録(アクセスログ)
とあるサイトの禁書目録(アクセスログ)ybenjo
 
首都圏における帰宅困難者のモデリング 中間報告
首都圏における帰宅困難者のモデリング 中間報告首都圏における帰宅困難者のモデリング 中間報告
首都圏における帰宅困難者のモデリング 中間報告ybenjo
 
Predicting Cancel Users in Offline Events
Predicting Cancel Users in Offline EventsPredicting Cancel Users in Offline Events
Predicting Cancel Users in Offline Eventsybenjo
 
首都圏における帰宅困難者のモデリング 最終報告
首都圏における帰宅困難者のモデリング 最終報告首都圏における帰宅困難者のモデリング 最終報告
首都圏における帰宅困難者のモデリング 最終報告ybenjo
 
Link prediction
Link predictionLink prediction
Link predictionybenjo
 
Modeling intransitivity in matchup and comparison data (WSDM 2016)
Modeling intransitivity in matchup and comparison data (WSDM 2016)Modeling intransitivity in matchup and comparison data (WSDM 2016)
Modeling intransitivity in matchup and comparison data (WSDM 2016)ybenjo
 
anohana
anohanaanohana
anohanaybenjo
 

Viewers also liked (16)

Nonlinear latent factorization by embedding multiple user interests(Recsys 2013)
Nonlinear latent factorization by embedding multiple user interests(Recsys 2013)Nonlinear latent factorization by embedding multiple user interests(Recsys 2013)
Nonlinear latent factorization by embedding multiple user interests(Recsys 2013)
 
R's anti sparseness
R's anti sparsenessR's anti sparseness
R's anti sparseness
 
useR!2010 matome
useR!2010 matomeuseR!2010 matome
useR!2010 matome
 
Topic Model Survey (wsdm2012)
Topic Model Survey (wsdm2012)Topic Model Survey (wsdm2012)
Topic Model Survey (wsdm2012)
 
Overcoming browser cookie churn with clustering in wsdm2012 reading
Overcoming browser cookie churn with clustering in wsdm2012 readingOvercoming browser cookie churn with clustering in wsdm2012 reading
Overcoming browser cookie churn with clustering in wsdm2012 reading
 
AJACS17
AJACS17AJACS17
AJACS17
 
AJACS HONGO8 (mining in DBCLS)
AJACS HONGO8 (mining in DBCLS)AJACS HONGO8 (mining in DBCLS)
AJACS HONGO8 (mining in DBCLS)
 
Personalized next-song recommendation in online karaokes(Recsys 2013)
Personalized next-song recommendation in online karaokes(Recsys 2013)Personalized next-song recommendation in online karaokes(Recsys 2013)
Personalized next-song recommendation in online karaokes(Recsys 2013)
 
patent analysis(LDA) and spotfire
patent analysis(LDA) and spotfirepatent analysis(LDA) and spotfire
patent analysis(LDA) and spotfire
 
とあるサイトの禁書目録(アクセスログ)
とあるサイトの禁書目録(アクセスログ)とあるサイトの禁書目録(アクセスログ)
とあるサイトの禁書目録(アクセスログ)
 
首都圏における帰宅困難者のモデリング 中間報告
首都圏における帰宅困難者のモデリング 中間報告首都圏における帰宅困難者のモデリング 中間報告
首都圏における帰宅困難者のモデリング 中間報告
 
Predicting Cancel Users in Offline Events
Predicting Cancel Users in Offline EventsPredicting Cancel Users in Offline Events
Predicting Cancel Users in Offline Events
 
首都圏における帰宅困難者のモデリング 最終報告
首都圏における帰宅困難者のモデリング 最終報告首都圏における帰宅困難者のモデリング 最終報告
首都圏における帰宅困難者のモデリング 最終報告
 
Link prediction
Link predictionLink prediction
Link prediction
 
Modeling intransitivity in matchup and comparison data (WSDM 2016)
Modeling intransitivity in matchup and comparison data (WSDM 2016)Modeling intransitivity in matchup and comparison data (WSDM 2016)
Modeling intransitivity in matchup and comparison data (WSDM 2016)
 
anohana
anohanaanohana
anohana
 

Similar to Preserving Personalized Pagerank in Subgraphs(ICML 2011)

Comparing estimation algorithms for block clustering models
Comparing estimation algorithms for block clustering modelsComparing estimation algorithms for block clustering models
Comparing estimation algorithms for block clustering modelsBigMC
 
Solovay Kitaev theorem
Solovay Kitaev theoremSolovay Kitaev theorem
Solovay Kitaev theoremJamesMa54
 
One way to see higher dimensional surface
One way to see higher dimensional surfaceOne way to see higher dimensional surface
One way to see higher dimensional surfaceKenta Oono
 
分かりやすいパターン認識第8章 学習アルゴリズムの一般化
分かりやすいパターン認識第8章 学習アルゴリズムの一般化分かりやすいパターン認識第8章 学習アルゴリズムの一般化
分かりやすいパターン認識第8章 学習アルゴリズムの一般化Yohei Sato
 
関西NIPS+読み会発表スライド
関西NIPS+読み会発表スライド関西NIPS+読み会発表スライド
関西NIPS+読み会発表スライドYuchi Matsuoka
 
H function and a problem related to a string
H function and a problem related to a stringH function and a problem related to a string
H function and a problem related to a stringAlexander Decker
 
修士論文発表会
修士論文発表会修士論文発表会
修士論文発表会Keikusl
 
Formulario Geometria Analitica.pdf
Formulario Geometria Analitica.pdfFormulario Geometria Analitica.pdf
Formulario Geometria Analitica.pdfAntonio Guasco
 
Trilinear embedding for divergence-form operators
Trilinear embedding for divergence-form operatorsTrilinear embedding for divergence-form operators
Trilinear embedding for divergence-form operatorsVjekoslavKovac1
 
On maximal and variational Fourier restriction
On maximal and variational Fourier restrictionOn maximal and variational Fourier restriction
On maximal and variational Fourier restrictionVjekoslavKovac1
 
GradStudentSeminarSept30
GradStudentSeminarSept30GradStudentSeminarSept30
GradStudentSeminarSept30Ryan White
 
11.0003www.iiste.org call for paper.common fixed point theorem for compatible...
11.0003www.iiste.org call for paper.common fixed point theorem for compatible...11.0003www.iiste.org call for paper.common fixed point theorem for compatible...
11.0003www.iiste.org call for paper.common fixed point theorem for compatible...Alexander Decker
 
3.common fixed point theorem for compatible mapping of type a -21-24
3.common fixed point theorem for compatible mapping of type a -21-243.common fixed point theorem for compatible mapping of type a -21-24
3.common fixed point theorem for compatible mapping of type a -21-24Alexander Decker
 
Hidden Markov Models common probability formulas
Hidden Markov Models common probability formulasHidden Markov Models common probability formulas
Hidden Markov Models common probability formulasNidhal Selmi
 
統計的学習の基礎 4章 前半
統計的学習の基礎 4章 前半統計的学習の基礎 4章 前半
統計的学習の基礎 4章 前半Ken'ichi Matsui
 

Similar to Preserving Personalized Pagerank in Subgraphs(ICML 2011) (20)

Comparing estimation algorithms for block clustering models
Comparing estimation algorithms for block clustering modelsComparing estimation algorithms for block clustering models
Comparing estimation algorithms for block clustering models
 
Solovay Kitaev theorem
Solovay Kitaev theoremSolovay Kitaev theorem
Solovay Kitaev theorem
 
One way to see higher dimensional surface
One way to see higher dimensional surfaceOne way to see higher dimensional surface
One way to see higher dimensional surface
 
分かりやすいパターン認識第8章 学習アルゴリズムの一般化
分かりやすいパターン認識第8章 学習アルゴリズムの一般化分かりやすいパターン認識第8章 学習アルゴリズムの一般化
分かりやすいパターン認識第8章 学習アルゴリズムの一般化
 
関西NIPS+読み会発表スライド
関西NIPS+読み会発表スライド関西NIPS+読み会発表スライド
関西NIPS+読み会発表スライド
 
H function and a problem related to a string
H function and a problem related to a stringH function and a problem related to a string
H function and a problem related to a string
 
修士論文発表会
修士論文発表会修士論文発表会
修士論文発表会
 
Formulario Geometria Analitica.pdf
Formulario Geometria Analitica.pdfFormulario Geometria Analitica.pdf
Formulario Geometria Analitica.pdf
 
Trilinear embedding for divergence-form operators
Trilinear embedding for divergence-form operatorsTrilinear embedding for divergence-form operators
Trilinear embedding for divergence-form operators
 
Image denoising
Image denoisingImage denoising
Image denoising
 
Hw5sols
Hw5solsHw5sols
Hw5sols
 
On maximal and variational Fourier restriction
On maximal and variational Fourier restrictionOn maximal and variational Fourier restriction
On maximal and variational Fourier restriction
 
GradStudentSeminarSept30
GradStudentSeminarSept30GradStudentSeminarSept30
GradStudentSeminarSept30
 
11.0003www.iiste.org call for paper.common fixed point theorem for compatible...
11.0003www.iiste.org call for paper.common fixed point theorem for compatible...11.0003www.iiste.org call for paper.common fixed point theorem for compatible...
11.0003www.iiste.org call for paper.common fixed point theorem for compatible...
 
3.common fixed point theorem for compatible mapping of type a -21-24
3.common fixed point theorem for compatible mapping of type a -21-243.common fixed point theorem for compatible mapping of type a -21-24
3.common fixed point theorem for compatible mapping of type a -21-24
 
Hidden Markov Models common probability formulas
Hidden Markov Models common probability formulasHidden Markov Models common probability formulas
Hidden Markov Models common probability formulas
 
test
testtest
test
 
統計的学習の基礎 4章 前半
統計的学習の基礎 4章 前半統計的学習の基礎 4章 前半
統計的学習の基礎 4章 前半
 
確率伝播
確率伝播確率伝播
確率伝播
 
rinko2011-agh
rinko2011-aghrinko2011-agh
rinko2011-agh
 

Recently uploaded

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j
 
Benefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksBenefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksSoftradix Technologies
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024Scott Keck-Warren
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
Pigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxOnBoard
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesSinan KOZAK
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersEnhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersThousandEyes
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhisoniya singh
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxMaking_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxnull - The Open Security Community
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 

Recently uploaded (20)

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
 
Benefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksBenefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other Frameworks
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
Pigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping Elbows
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptx
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersEnhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxMaking_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 

Preserving Personalized Pagerank in Subgraphs(ICML 2011)

  • 1. Preserving Personalized Pagerank in Subgraphs (Andrea Vattani, Deepayan Chakrabarti, Maxim Gurevich) 1
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8. a a b c c
  • 9.
  • 10.
  • 11.
  • 12.
  • 13. “Due to space constraints, complete proofs of our claims will appear in the full version of the paper.”
  • 14.
  • 15.
  • 16. p = αr + (1 − α)A D t −1 p p α r A D
  • 18. [t] pi (j) = α(1 − α)pi (j) t=0 t [t] 1 pi (j) = d+ (kl ) k1 =i,k2 ,··· ,kt+1 =j l=1
  • 19.
  • 20. G = (V, E) S⊂V p G pG[S] ˜ p G
  • 21. ˜ min d(p , p G G[S] )
  • 22. |S| = o(n/ log n) 1/2 − o(1) Ω(|S|) S ⊂S ∗ u S ∗ S u ∗ u
  • 23.
  • 24.
  • 25.
  • 26. wG (i, j) = 1 j∈V
  • 27. G = (V, wG ) S⊂V H = (S ∪ SIN K, wH )
  • 28. pi (j) G = pi (j) H ∀i, j ∈ S
  • 29.
  • 30.
  • 31. a a sampling a,c b SINK Remove b c c
  • 32. ∞ wH (x, y)+ = (1 − α)wG (x, z)wG (z, y) [(1 − α)wG (z, z)] t t=0
  • 33. wH (x, SIN K)+ = wG (x, z) − wH (x, y) y=z;wG (z,y)>0
  • 34.
  • 35.
  • 36. P H pi H = pi G WH 1 t WH = (I − α(P ) ) H −1 1−α O(E + |S| ) 2
  • 37.
  • 38. wH (u, v) < τ wH (u, SIN K)+ = wH (u, v), wH (u, v) = 0
  • 39.
  • 40.
  • 41.
  • 42. ||P (j) − P (j)||1 H R 1−α ≤ 2τ |S| + 1 α P (j) H P (j) R
  • 43.
  • 44.
  • 45. v= pj (i), G i∈S j∈V S r(j) = r(j ), j, j ∈ V S pr (i), ∀i ∈ S G
  • 46.
  • 47.
  • 48.
  • 49. SINK Page Rank
  • 50.
  • 51. 1 1 wH (SOU RCE, l) = pj G (k)wG (k.l), l ∈ S |V S| α j,k∈V S wH (SOU RCE, SIN K) = 1 − wH (SOU RCE, i) i∈S
  • 52.
  • 53. pr (i) = pr G H (i), ∀i ∈ S   r(k), k∈S r (k) = ρ|V S|, k = SOU RCE  0, k = SIN K

Editor's Notes

  1. \n
  2. \n
  3. \n
  4. \n
  5. \n
  6. \n
  7. \n
  8. \n
  9. \n
  10. \n
  11. \n
  12. \n
  13. \n
  14. \n
  15. \n
  16. \n
  17. \n
  18. \n
  19. \n
  20. \n
  21. \n
  22. \n
  23. \n
  24. \n
  25. \n
  26. \n
  27. \n
  28. \n
  29. \n
  30. \n
  31. \n
  32. \n
  33. \n
  34. \n
  35. \n
  36. \n
  37. \n
  38. \n
  39. \n
  40. \n
  41. \n
  42. \n
  43. \n
  44. \n
  45. \n
  46. \n
  47. \n
  48. \n
  49. \n
  50. \n
  51. \n
  52. \n
  53. \n
  54. \n
  55. \n
  56. \n
  57. \n
  58. \n
  59. \n
  60. \n
  61. \n
  62. \n
  63. \n
  64. \n
  65. \n
  66. \n
  67. \n
  68. \n
  69. \n