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USC Computer Science Technical Reports, no. 817 (2004)
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USC Computer Science Technical Reports, no. 817 (2004)
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! ! ! ! " ! # $ ! ! % & ! ’ () * + & , - ! ./ 0 1 2 3 * #$ 4 # $ * 4 4 5 3 # $ 3 *! 3 % ! 6 () 6 % # ()$ ! & 4 " % () # 17$ ! Æ ! ! ! 8 * # 1111 $ /11 + * 6 9 6 4 : ; 6 () ; " # $ %! # &’ # ( ) 5 ’ 6 ! () % * 6 ! # $ - # $ * ! ! 8 * * # .0 1 2$ # $ ! ! & # $ ’ < # $ ’ 6 4 + = + > * + / + 7 Æ * + ? @ .> ? A 2 9 , B ;6+ .C 2 .> 2 ;6+ ! " ! * 3 ;6+ ’ & D - .0 1 2 6 4 : .= 2 ;6+ & B # $ 5 6 ;6+ 6 ( ) ;6+ 8 * " , 3 # $ , # $ # $ , 3 #$ ! ! # @ $ ! * &’ & ’ &’ # = E # $ E 3 * % F # $ # $ ! # $E F ! B 3 3 3 ’ ! B # $ E # $G E E # $E # $ # $ () # $ 6 ! # $ " 4 ’ # $ () : 6 ! # $E ! # 3 * 3 $ B # $ * &+’ # , - . ( ! # ! 6 6 .> 2 1 7 ! A * F - & + ! + ! ! 5 H ! 5 6 & + @ .7 2 6 01 IF 8 * () A=/=0 @ .? 2 3 ./ 2 ! - ! * ’ ! * 6 + * 11 111 =111 11111 6 # $ 8 # + 7= $ # $ # $ E1 7 8 J = > 1 10 100 1000 10000 100000 0 20000 40000 60000 80000 100000 new rank (in log) old rank Original PageRank: PR change due to collusions in Web Collusion200 pseudo collusion 8 B B : 3.5 4 4.5 5 5.5 6 6.5 7 0 20000 40000 60000 80000 100000 (new PageRank weight)/(old PageRank weight) old rank Original PageRank: PR change due to collusions in Web Collusion200 8 =B B : #4 $ 8 ! 3 >7 , ! 77 K 1 8 * 111= /7 11117 71>> 1111C 71>0 11 =11 1111 4 8 > J / 3 1 10 100 1000 10000 0 2000 4000 6000 8000 10000 new rank (in log) old rank Original PageRank: PR change due to collusions in Blog Collusion200 pseudo collusion 8 >B B : 4 4.5 5 5.5 6 6.5 7 0 2000 4000 6000 8000 10000 (new PageRank weight)/(old PageRank weight) old rank Original PageRank: PR change due to collusions in Blog Collusion200 8 /B B : #4 $ ! % 8 * 11 1 A 0 011 0AA 6 #= $ ! " 8 8 7 ! : ! 8 7 6 # $ / 0 1 2 3 4 5 6 7 8 0 10 20 30 40 50 60 70 80 90 100 Amplification factor Colluding groups Web: amplification factor of colluding groups in Collusion200 Web: Original PR Blog: Original PR 8 7B 3! 11 H=11 1e-07 1e-06 1e-05 0.0001 0.001 0.01 0.1 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 PR value (in log) Ranked nodes (normalized) PageRank value distribution Web Blog PLRG Random Graph Web - top 1M nodes Blog - 72428 nodes Random Graph - 10000 nodes PLRG - 10000 nodes 8 ?B / @ ! 5 8 ? # $ # #F $ . 2 .=2 $ 8 " + ! " " & 6 " # 1$ * ! ! # $ ! ; * * - 6 ! ! F * . 2 . 2 * 8 & , & H = # $ 6 * I * * # $ * , # % () 6 : 1 1 : % G G 6 * ! EA E1 = 8 A 1 # $ A # $ 6 / # , + 7 ; 1 10 100 1000 10000 100000 1e+06 1 10 100 1000 Frequency Revisit interval Revisit distribution for star topology with a cheater Hub node Cheating node 8 AB 8 & # A$ # 1$ 6 111111 6 ’ ! * ! # !$ ; H ! 4 * 4 - # $ # $ 6 : ; .7 2 D * H 6 * # # $$ 6 * ; * 6 9 , ! 6 * ! ’ * * H # 4 $ 6 " ; @ ; - ! 6 * 6 + % F # $ # $ % # $E # G# $ $ # $E # G# $ $ # $ # $ 6 # $ # $ 1 - * B 8 0 1 6 ’ ! 9 ’ ’ & ! ! 6 0 Æ 1, ( 1 ? 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 PDF Co-Co range PDF of Co-Co distribution in Web & Blog Web Blog 8 0B D8 B .1 12 . 12 6 B H #$ - #$ H Æ F # $ # $E # $ 15 1 B # $ = 4 #$ : 4 E # ! ! # $$ #$ 6 4 6 # ! ! # $$ * B E E G#1 7 $ # $ 6 6 - - * 6 , 0 1 2 3 4 5 6 7 8 0 10 20 30 40 50 60 70 80 90 100 Amplification factor Colluding groups Web: amplification factor of colluding groups in Collusion200 Original PR Adaptive-epsilon - Exp Adaptive-epsilon - Linear 8 CB B ! 11 H=11 3 + > * * 9 1 ? 1 /7 1 > 1 7 1 1A 7 1 17 1 1>A7 9 1 7 = > 6 * ! * + > 3 + >= , K ! ! # $ 1 H # 3 * @$ ! * 3 8 C 1 ! & A 0 1 2 3 4 5 6 7 8 0 10 20 30 40 50 60 70 80 90 100 Amplification factor Colluding groups Blog: amplification factor of colluding groups in Collusion200 Original PR Adaptive-epsilon - Exp Adaptive-epsilon - Linear 8 1B B ! 11 H=11 8 = # $ 8 ! ! 6 8 - #8 > /$ ’ * == # 1 $ > B 1 4 3 111 = 1 4 C 6 7111 1111 > = 4 ’ 10 100 1000 10000 100000 1e+06 0 20 40 60 80 100 120 140 160 180 200 rank (in log) colluding nodes PR change due to Collusion200 in Web original PR: old rank before collusion original PR: new rank after collusion adaptiv-epsilon (Exp): old rank before collusion adaptiv-epsilon (Exp): new rank after collusion 8 B B 3 10 100 1000 10000 0 20 40 60 80 100 120 140 160 180 200 weight (in log) colluding nodes PR change due to Collusion200 in Web - Avg. PR weight =1 original PR: old weight before collusion original PR: new weight after collusion adaptiv-epsilon (Exp): old weight before collusion adaptiv-epsilon (Exp): new weight after collusion 8 =B B 3 71 C111 * ! # " 1 C?$ 8 7 3 8 = ! 8 ? A # $ 6 0 1 10 100 1000 10000 100000 0 20 40 60 80 100 120 140 160 180 200 rank (in log) colluding nodes PR change due to Collusion200 in Blog original PR: old rank before collusion original PR: new rank after collusion adaptiv-epsilon (Exp): old rank before collusion adaptiv-epsilon (Exp): new rank after collusion 8 >B B 3 0.1 1 10 100 1000 0 20 40 60 80 100 120 140 160 180 200 weight (in log) colluding nodes PR change due to Collusion200 in Blog - Avg. PR weight =1 original PR: old weight before collusion original PR: new weight after collusion adaptiv-epsilon (Exp): old weight before collusion adaptiv-epsilon (Exp): new weight after collusion 8 /B B 3 F 8 A 6 8 ? = 6 # F ! - > ! B 0?CA = 7/ , 1 !" # + / * 0 1 2 3 4 5 6 7 8 1 2 3 Amplification factor Colluding groups Web: amplification factor of colluding groups in Collusion22 Original PR Adaptive-epsilon - Exp Adaptive-epsilon - Linear 8 7B B 3! > H== ( ) * ! B 111 1 CC0 CCA 1 ! CC0 CCC #: * $ 8 0 1 # $ # $ CC0 CCC # $ 6 * 4 E1 7 H CC0 CCC Æ 6 # # $ " 8 C # > $ >1>A>A?0 >1>A>A?0 IF $%% % >1>>?C?A IF $%% % 4 8 =1 # > $ A> " # ! # ( 2 . ! %3 4/ $ %! 5,67 C 10 100 1000 10000 100000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 rank (in log) colluding nodes PR change due to collusion22 in Web G1 G2 original PR: old rank before collusion original PR: new rank after collusion adaptiv-epsilon (Exp): old rank before collusion adaptiv-epsilon (Exp): new rank after collusion 8 ?B B 3 100 1000 10000 100000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 weight (in log) colluding nodes PR change due to collusion22 in Web - Avg. PR weight =1 G1 G2 original PR: old weight before collusion original PR: new weight after collusion adaptiv-epsilon (Exp): old weight before collusion adaptiv-epsilon (Exp): new weight after collusion 8 AB B 3 A ?77C % " 8 6 =7 IF # $ # $ ’ ( ) =7 IF : $%% % ! $%% % =7 6 ! L / 0 6 71 & 6 ( !) * 0 0.5 1 1.5 2 2.5 3 3.5 4 0 0.1 0.2 0.3 0.4 0.5 0.6 PageRank weight (normalized by its value at 0.15 reset prob.) epsilon: the reset probability PageRank Reset Perturbation Star + Dangleing circle topology F= 0.15/epsilon node_0 node_1 node_998 node_999 8 0B 6 B G " # $ % B ’ 4 * Æ * + 6 4 ( ) % + 1 ’ : BMM M BMM M = BMM M BMM M > BMM M BMMM / BMMM BMM M 7 BMM M BMM M ? BMM M BMMM A BMMM BMMM M 0 BMMMM C BMM M 1 BMM M BMM M BMMM BMM*M= = BMM M BMM M > BMMM M BMM M / BMM M BMM M 7 BMM M BMM>M ? BMM M BMM M A BMM M BMM M 0 BMM>M C BMM M BMMM MM =1 BMM M BMM M = BMM M BMMM == BMM*M= BMMM => BMMM M BMM M =/ BMM MM BMM M =7 BMM M BMMM M 6 B 6 =7 .2 !" ### .=2 $ $ % & ’" ()*+ .>2 , Æ & %" - . " (/(0(/) ##( ./2 , , 1 2 1 3 4 5 6 . % "" 7 $6 8 ## .72 7 - ! ! )/"(9 , " - : & ())/ .?2 . % 2 6 8 . - (#;<(/"()) ##( .A2 $ 1 " # $ % 6 . & % ""% $6 ;8 +"9 ##< ## .02 = 3 - 4 1"- & # ’ (" % " ## .C2 8 - = ) & 8 - > ?9 @+ %9#?" 9 ())) .12 4! = 4 * # + , 6 . % " " 7 $6 8 ## .2 @ - % 2 6 2 5 6 & 2 6 " 2 %#") 43 = 1 3 ## .=2 A @ B 5 - & 8 - . . & 8 C & ;&8&< ##( 30373768 30336967 8 CB = 71311 65519 45403 65522 71320 71314 71319 42762 42141 65520 8 =1B .>2 % $ 2 - ! , # ! / , ! % D ())* ./2 $ C #00" 0"0 .72 ! $ % D #00""" 0 0 ’0/0 .?2 ! B-"2% 3 = & & F # $ B ; < E ; <F F ; < ; <F F ;( < ; < ; E E < ; <F F;( < ; < 6 ; < ; <F ; < ’ % ( Æ * 6 - * # $ + # $ * $ $ # D 8 * D * 8 = == # $ 8 = == 8 =B & - ; - ! 10 100 1000 10000 100000 1e+06 1e+07 1e+08 0 20 40 60 80 100 120 140 160 180 200 rank (in log) colluding nodes PR change due to Collusion200 in Web original PR: old rank before collusion original PR: new rank after collusion adaptiv-epsilon (Exp): old rank before collusion adaptiv-epsilon (Exp): new rank after collusion 8 =B 3 #* $ H =11 1 10 100 1000 10000 0 20 40 60 80 100 120 140 160 180 200 weight (in log) colluding nodes PR change due to Collusions in Web - Avg. PR weight =1 original PR: old weight before collusion original PR: new weight after collusion adaptiv-epsilon (Exp): old weight before collusion adaptiv-epsilon (Exp): new weight after collusion 8 ==B 3 #* $ H =11 ( ) 6 1N 1 C # 8 0$ 4 * * 8 =>B ! ! & ) ( * + % # $ & # $ 1 2 3 4 5 6 7 8 0 10 20 30 40 50 60 70 80 90 100 Amplification factor Colluding groups Web: amplification factor of colluding groups in Collusion200 (arbitrary coco) Original PR Adaptive-epsilon - Exp Adaptive-epsilon - Linear 8 =>B 3! 11 # $ ’# $ # $ 6 4 / * 4 # $ # $ 4 6 *4 % # $ & # $ , *4 ’# $ ; 4 B # $ ! 4 *4 6 .72 B # $ ! 4 * 4 6 * - 6 * , 6 : ; HFOI 4 : ;
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Description
Hui Zhang, Ashish Goel, Ramesh Govindan, Kahn Mason, Benjamin Van Roy. "Making eigenvector-based reputation systems robust to collusion." Computer Science Technical Reports (Los Angeles, California, USA: University of Southern California. Department of Computer Science) no. 817 (2004).
Asset Metadata
Creator
by Hui Zhang, Ashish Goel, Ramesh Govindan, Kahn Mason, Benjamin Van Roy
(author)
Core Title
USC Computer Science Technical Reports, no. 817 (2004)
Alternative Title
Making eigenvector-based reputation systems robust to collusion (
title
)
Publisher
Department of Computer Science,USC Viterbi School of Engineering, University of Southern California, 3650 McClintock Avenue, Los Angeles, California, 90089, USA
(publisher)
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OAI-PMH Harvest
Format
15 pages
(extent),
technical reports
(aat)
Language
English
Unique identifier
UC16270132
Identifier
04-817 Making Eigenvector-Based Reputation Systems Robust to Collusion (filename)
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usc-cstr-04-817
Format
15 pages (extent),technical reports (aat)
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Department of Computer Science (University of Southern California) and the author(s).
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In copyright - Non-commercial use permitted (https://rightsstatements.org/vocab/InC-NC/1.0/
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20180426-rozan-cstechreports-shoaf
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Computer Science Technical Report Archive
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University of Southern California. Department of Computer Science. Technical Reports
(series)
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The author(s) retain rights to their work according to U.S. copyright law. Electronic access is being provided by the USC Libraries, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.
Repository Name
USC Viterbi School of Engineering Department of Computer Science
Repository Location
Department of Computer Science. USC Viterbi School of Engineering. Los Angeles\, CA\, 90089
Repository Email
csdept@usc.edu
Inherited Values
Title
Computer Science Technical Report Archive
Description
Archive of computer science technical reports published by the USC Department of Computer Science from 1991 - 2017.
Coverage Temporal
1991/2017
Repository Email
csdept@usc.edu
Repository Name
USC Viterbi School of Engineering Department of Computer Science
Repository Location
Department of Computer Science. USC Viterbi School of Engineering. Los Angeles\, CA\, 90089
Publisher
Department of Computer Science,USC Viterbi School of Engineering, University of Southern California, 3650 McClintock Avenue, Los Angeles, California, 90089, USA
(publisher)
Copyright
In copyright - Non-commercial use permitted (https://rightsstatements.org/vocab/InC-NC/1.0/