程序代写代做代考 crawler database html data structure go Excel ER information retrieval algorithm cache Hive C js AI graph The readers

The readers
The PageRank Citation Ranking􏰅 Bringing Order to the Web
January 􏰈􏰊􏰆 􏰇􏰊􏰊􏰉
Abstract
imp ortance of a Web page is an inherently sub jective matter􏰆 which dep ends on the interests􏰆 knowledge and attitudes􏰍 But there is still much that can be said ob jectively
ab out the relative
rating Web pages attention devoted to them􏰍
imp ortance of Web pages􏰍 This pap er describ es PageRank􏰆 a metho d for ob jectively and mechanically􏰆 e􏰋ectively measuring the human interest and
We compare PageRank to an idealized random Web surfer􏰍 We show how to e􏰌ciently compute PageRank for large numb ers of pages􏰍 And􏰆 we show how to apply PageRank to search and to user navigation􏰍
􏰇 Intro duction and Motivation
The World Wide Web creates many new challenges for information retrieval􏰍 It is very large and heterogeneous􏰍 Current estimates are that there are over 􏰇􏰐􏰟 million web pages with a doubling life of less than one year􏰍 More imp ortantly􏰆 the web pages are extremely diverse􏰆 ranging from 􏰗What is Jo e having for lunch to day􏰢􏰗 to journals ab out information retrieval􏰍 In addition to these ma jor challenges􏰆 search engines on the Web must also contend with inexp erienced users and pages engineered to manipulate search engine ranking functions􏰍
However􏰆 unlike 􏰗􏰔at􏰗 do cument collections􏰆 the World Wide Web is hyp ertext and provides considerable auxiliary information on top of the text of the web pages􏰆 such as link structure and link text􏰍 In this pap er􏰆 we take advantage of the link structure of the Web to pro duce a global 􏰤imp ortance􏰗 ranking of every web page􏰍 This ranking􏰆 called PageRank􏰆 helps search engines and users quickly make sense of the vast heterogeneity of the World Wide Web􏰍
􏰇􏰍􏰇 Diversity of Web Pages
Although there is already a large literature on academic citation analysis􏰆 there are a numb er of signi􏰓cant di􏰋erences b etween web pages and academic publications􏰍 Unlike academic pap ers which are scrupulously reviewed􏰆 web pages proliferate free of quality control or publishing costs􏰍 With a simple program􏰆 huge numb ers of pages can b e created easily􏰆 arti􏰓cially in􏰔ating citation counts􏰍 Because the Web environment contains comp eting pro􏰓t seeking ventures􏰆 attention getting strategies evolve in resp onse to search engine algorithms􏰍 For this reason􏰆 any evaluation strategy
Further􏰆 academic pap ers of citations􏰆 as well as in vary on a much wider scale than academic pap ers in quality􏰆 usage􏰆 citations􏰆 and length􏰍 A random archived message p osting
which counts replicable features of web pages is prone to manipulation􏰍
are well de􏰓ned units of work􏰆
their purp ose 􏰦 to extend the b o dy of
roughly similar in knowledge􏰍
quality and numb er Web pages
􏰇

asking an obscure question ab out an IBM computer is very di􏰋erent from the IBM home page􏰍 A research article ab out the e􏰋ects of cellular phone use on driver attention is very di􏰋erent from an advertisement for a particular cellular provider􏰍 The average web page quality exp erienced by a user is higher than the quality of the average web page􏰍 This is b ecause the simplicity of creating and publishing web pages results in a large fraction of low quality web pages that users are unlikely to read􏰍
There are many primarily with one 􏰝
􏰇􏰍􏰈 PageRank
axes along which web pages may b e di􏰋erentiated􏰍 In this pap er􏰆 we deal an approximation of the overall relative imp ortance of web pages􏰍
In order to measure the relative imp ortance of web pages􏰆 we prop ose PageRank􏰆 a metho d for computing a ranking for every web page based on the graph of the web􏰍 PageRank has applications in search􏰆 browsing􏰆 and tra􏰌c estimation􏰍
Section 􏰈 gives a mathematical description of PageRank and provides some intuitive justi􏰓􏰝 cation􏰍 In Section 􏰎􏰆 we show how we e􏰌ciently compute PageRank for as many as 􏰐􏰇􏰉 million hyp erlinks􏰍 To test the utility of PageRank for search􏰆 we built a web search engine called Go ogle 􏰚Section 􏰐􏰛􏰍 We also demonstrate how PageRank can b e used as a browsing aid in Section 􏰒􏰍􏰎􏰍
􏰈 A Ranking for Every Page on the Web
􏰈􏰍􏰇 Related Work
There has b een a great deal of work on academic citation analysis 􏰣Gar􏰊􏰐 􏰥􏰍 Go􏰋man 􏰣Gof􏰒􏰇 􏰥 has published an interesting theory of how information 􏰔ow in a scienti􏰓c community is an epidemic pro cess􏰍
There has b een a fair amount of recent activity on how to exploit the link structure of large hyp ertext systems such as the web􏰍 Pitkow recently completed his Ph􏰍D􏰍 thesis on 􏰤Characterizing
World Wide Web
discuss clustering
discusses information that can b e obtained from the link structure for a variety
Go o d visualization demands added structure on the hyp ertext and is discussed in 􏰣MFH􏰊􏰐 􏰆 MF􏰊􏰐 􏰥􏰍 Recently􏰆 Kleinb erg 􏰣Kle􏰊􏰉 􏰥 has develop ed an interesting mo del of the web as Hubs and Authorities􏰆 based on an eigenvector calculation on the co􏰝citation matrix of the web􏰍
Ecologies􏰗 􏰣Pit􏰊􏰒 􏰆 PPR􏰊􏰑 􏰥 with a wide variety of link based metho ds that take the link structure into account 􏰣WVS􏰜 􏰊􏰑 􏰥􏰍
analysis􏰍 Weiss Sp ertus 􏰣Sp e􏰊􏰒􏰥 of applications􏰍
Finally􏰆 there has b een some interest in what 􏰤quality􏰗
munity It is citation a ma jor
p ointing This
means on the
net from a
the web􏰙s academic backlinks
library com􏰝
hyp ertextual citation􏰍 So􏰆 􏰚or citations􏰛
􏰣Til􏰥􏰍
obvious to try to apply standard
techniques to b eing like an thousands of
structure􏰍 page like
to it􏰍
citation analysis One can simply think of every link as
http􏰅􏰞􏰞www􏰍yaho o􏰍com􏰞
will have tens of
fact that the Yaho o home page has so many backlinks generally imply that it is quite imp ortant􏰍 Indeed􏰆 many of the web search engines have used backlink count as a way to try to bias their databases in favor of higher quality or more imp ortant pages􏰍 However􏰆 simple backlink counts have a numb er of problems on the web􏰍 Some of these problems have to do with characteristics of the web which are not present in normal academic citation databases􏰍
􏰈

􏰈􏰍􏰈 Link Structure of the Web
While estimates vary􏰆 the current graph of the crawlable Web has roughly 􏰇􏰐􏰟 million no des 􏰚pages􏰛 and 􏰇􏰍􏰒 billion edges 􏰚links􏰛􏰍 Every page has some numb er of forward links 􏰚outedges􏰛 and backlinks 􏰚inedges􏰛 􏰚see Figure 􏰇􏰛􏰍 We can never know whether we have found all the backlinks of a particular page but if we have downloaded it􏰆 we know all of its forward links at that time􏰍
Web pages vary greatly in terms of the
􏰈􏰍􏰎 Propagation of Ranking
A
Figure 􏰇􏰅 A and B are Backlinks of C
numb er of backlinks they have􏰍 For example􏰆 the home page has 􏰑􏰈􏰆􏰉􏰟􏰏 backlinks in our current database compared to most pages which
Netscap e
have just
few links􏰍
Prize 􏰣San􏰊􏰐􏰥􏰍 PageRank provides a more sophisticated metho d for doing citation counting􏰍
The reason that PageRank is interesting is that there are many cases where simple citation
a few
Simple citation counting has b een
backlinks􏰍 Generally􏰆 highly
linked pages are more 􏰤imp ortant􏰗 than pages with used to sp eculate on the future winners of the Nob el
counting do es not corresp ond to our page has a link o􏰋 the Yaho o home
than many pages with more
go o d an approximation to 􏰤imp ortance􏰗 can b e obtained just
Through Links
This page
PageRank
from the link structure􏰍
should b e ranked higher is an attempt to see how
links but from obscure places􏰍
common sense notion of imp ortance􏰍 For example􏰆 if a web page􏰆 it may b e just one link but it is a very imp ortant one􏰍
Based on the discussion ab ove􏰆 we give the following intuitive description of PageRank􏰅 a page has high rank if the sum of the ranks of its backlinks is high􏰍 This covers b oth the case when a page has many backlinks and when a page has a few highly ranked backlinks􏰍
􏰈􏰍􏰏 De􏰓nition of PageRank
Let u be a web page􏰍 Then let Fu be the set of pages u points to and Bu be the set of pages that p oint to u􏰍 Let Nu 􏰡 jFu j b e the numb er of links from u and let c b e a factor used for normalization
􏰚so
that the total rank of all web pages is constant􏰛􏰍
We b egin by
de􏰓ning a simple ranking􏰆 R which is a slightly simpli􏰓ed version of PageRank􏰅
R􏰚u􏰛 􏰡 c
X v 􏰈Bu
R􏰚v 􏰛 Nv
􏰎
C
B

Then􏰆 the
PageRank
of a set
to the
Figure 􏰈􏰅
50
3 50
3 3
Simpli􏰓ed PageRank Calculation
100
9
This formalizes the intuition in the previous section􏰍 Note that the rank of a page is divided among its forward links evenly to contribute to the ranks of the pages they p oint to􏰍 Note that c 􏰨 􏰇 b ecause there are a numb er of pages with no forward links and their weight is lost from the system 􏰚see section 􏰈􏰍􏰒􏰛􏰍 The equation is recursive but it may b e computed by starting with any set of ranks and iterating the computation until it converges􏰍 Figure 􏰈 demonstrates the propagation of rank from one pair of pages to another􏰍 Figure 􏰎 shows a consistent steady state solution for a set of pages􏰍
Stated another way􏰆 let A b e a square matrix with the rows and column corresp onding to web pages􏰍 Let Au􏰠v 􏰡 􏰇􏰡Nu if there is an edge from u to v and Au􏰠v 􏰡 􏰟 if not􏰍 If we treat R as a vector over web pages􏰆 then we have R 􏰡 cAR 􏰍 So R is an eigenvector of A with eigenvalue c􏰍 In fact􏰆 we want the dominant eigenvector of A􏰍 It may b e computed by rep eatedly applying A to any nondegenerate start vector􏰍
There is a small problem with this simpli􏰓ed ranking function􏰍 Consider two web pages that
p oint one of 􏰚since
To
De􏰓nition 􏰇 Let E 􏰚u􏰛
to each other but to no other page􏰍 And supp ose there is some web page which p oints to them􏰍 Then􏰆 during iteration􏰆 this lo op will accumulate rank but never distribute any rank
there are no outedges􏰛􏰍 The lo op forms a sort of trap which we call a rank sink􏰍
overcome this problem of rank sinks􏰆 we intro duce a rank
source􏰅 corresponds
the Web pages that of Web pages is an assignment􏰆 R􏰟 􏰆
source which
of rank􏰍 satis􏰓es
􏰚􏰇􏰛
be some vector over
to a Web pages
􏰟
R 􏰚u􏰛 􏰡 c
jjR 􏰟 jj􏰇 􏰡 􏰇
X R􏰟 􏰚v 􏰛
Nv v 􏰈Bu
􏰜 cE􏰚u􏰛
such that
c is
maximized and
􏰚jjR 􏰟 jj􏰇 denotes the L􏰇
norm of
R 􏰟 􏰛􏰍
􏰏
53
50

is some
vector over the web pages that corresp onds to a
is all p ositive􏰆 c must b e reduced to balance the equation􏰍
A 0.4
Surfer Mo del

0.2
0.2
0.2 0.4
Figure 􏰎􏰅 Simpli􏰓ed PageRank
Calculation


Figure 􏰏􏰅
where
tion 􏰑􏰛􏰍 Note that if E
technique corresp onds to a decay factor􏰍 In matrix notation we have R 􏰟 􏰡 c􏰚AR 􏰟 􏰜 E 􏰛􏰍 Since jjR􏰟 jj􏰇 􏰡 􏰇􏰆 we can rewrite this as R􏰟 􏰡 c􏰚A 􏰜 E 􏰪 􏰇􏰛R􏰟 where 􏰇 is the vector consisting of all ones􏰍 So􏰆 R􏰟 is an eigenvector of 􏰚A 􏰜 E 􏰪 􏰇􏰛􏰍
Lo op Which Acts as a Rank Sink
E 􏰚u􏰛
􏰈􏰍􏰐 Random
The de􏰓nition of
simpli􏰓ed version
graph of the Web􏰍 Intuitively􏰆 this can b e thought of as mo deling the b ehavior of a 􏰤random surfer􏰗􏰍 The 􏰤random surfer􏰗 simply keeps clicking on successive links at random􏰍 However􏰆 if a real Web surfer ever gets into a small lo op of web pages􏰆 it is unlikely that the surfer will continue in the lo op forever􏰍 Instead􏰆 the surfer will jump to some other page􏰍 The additional factor E can b e viewed as a way of mo deling this b ehavior􏰅 the surfer p erio dically 􏰤gets b ored􏰗 and jumps to a
PageRank ab ove has another intuitive basis in random corresp onds to the standing probability distribution of
walks on graphs􏰍
a random walk on the
􏰐
B 0.2
C 0.4
source
of rank 􏰚see Sec􏰝 Therefore􏰆 this
The

random page chosen based on the distribution in E􏰍
So far we have left E as a user de􏰓ned parameter􏰍 In most tests we let E b e uniform over all
web pages with value 􏰋􏰍 However􏰆 in Section 􏰑 we show how di􏰋erent values of E can generate 􏰤customized􏰗 page ranks􏰍
􏰈􏰍􏰑 Computing PageRank
The computation
of PageRank is
fairly straightforward if we ignore
the issues of scale􏰍
Let S b e as follows􏰅
almost
any vector
over Web
pages 􏰚for example
R􏰟 􏰫 lo op 􏰅
Ri􏰜􏰇 􏰫 d 􏰫
Ri􏰜􏰇 􏰫 􏰌 􏰫
while 􏰌 􏰩 􏰧
E 􏰛􏰍 Then PageRank may S
ARi
jjRi jj􏰇 􏰆 jjRi􏰜􏰇 jj􏰇 Ri􏰜􏰇 􏰜 dE
jjRi􏰜􏰇 􏰆 Ri jj􏰇
b e
computed
Note that
normalization is to multiply R by the appropriate factor􏰍 The use of d may have a small impact on the in􏰔uence of E 􏰍
􏰈􏰍􏰒 Dangling Links
One issue with this mo del is dangling links􏰍 Dangling links are simply links that p oint to any page with no outgoing links􏰍 They a􏰋ect the mo del b ecause it is not clear where their weight should b e distributed􏰆 and there are a large numb er of them􏰍 Often these dangling links are simply pages that we have not downloaded yet􏰆 since it is hard to sample the entire web 􏰚in our 􏰈􏰏 million pages currently downloaded􏰆 we have 􏰐􏰇 million URLs not downloaded yet􏰆 and hence dangling􏰛􏰍 Because dangling links do not a􏰋ect the ranking of any other page directly􏰆 we simply remove them from the system until all the PageRanks are calculated􏰍 After all the PageRanks are calculated􏰆 they can b e added back in􏰆 without a􏰋ecting things signi􏰓cantly􏰍 Notice the normalization of the other links on the same page as a link which was removed will change slightly􏰆 but this should not have a large e􏰋ect􏰍
􏰎 Implementation
As part of the Stanford WebBase pro ject 􏰣PB􏰊􏰉􏰥􏰆 we have built a complete crawling and indexing system with a current rep ository of 􏰈􏰏 million web pages􏰍 Any web crawler needs to keep a database of URLs so it can discover all the URLs on the web􏰍 To implement PageRank􏰆 the web crawler simply needs to build an index of links as it crawls􏰍 While a simple task􏰆 it is non􏰝trivial b ecause of the huge volumes involved􏰍 For example􏰆 to index our current 􏰈􏰏 million page database in ab out 􏰓ve days􏰆 we need to pro cess ab out 􏰐􏰟 web pages p er second􏰍 Since there ab out ab out 􏰇􏰇 links on an average page 􏰚dep ending on what you count as a link􏰛 we need to pro cess 􏰐􏰐􏰟 links p er second􏰍 Also􏰆 our database of 􏰈􏰏 million pages references over 􏰒􏰐 million unique URLs which each link must b e compared against􏰍
the d
factor
increases the rate of
convergence and maintains jjRjj􏰇 􏰍 An alternative
􏰑

Much time has b een sp ent making the system resilient in the face of many deeply and intricately 􏰔awed web artifacts􏰍 There exist in􏰓nitely large sites􏰆 pages􏰆 and even URLs􏰍 A large fraction of web pages have incorrect HTML􏰆 making parser design di􏰌cult􏰍 Messy heuristics are used to help the crawling pro cess􏰍 For example􏰆 we do not crawl URLs with 􏰞cgi􏰝bin􏰞 in them􏰍 Of course it is imp ossible to get a correct sample of the 􏰗entire web􏰗 since it is always changing􏰍 Sites are sometimes down􏰆 and some p eople decide to not allow their sites to b e indexed􏰍 Despite all this􏰆 we b elieve we have a reasonable representation of the actual link structure of publicly accessible web􏰍
􏰎􏰍􏰇 PageRank Implementation
We convert each URL into a unique integer􏰆 and store each hyp erlink in a database using the integer IDs to identify pages􏰍 Details of our implementation are in 􏰣PB􏰊􏰉 􏰥􏰍 In general􏰆 we have implemented PageRank in the following manner􏰍 First we sort the link structure by Parent ID􏰍 Then dangling links are removed from the link database for reasons discussed ab ove 􏰚a few iterations removes the vast ma jority of the dangling links􏰛􏰍 We need to make an initial assignment of the ranks􏰍 This assignment can b e made by one of several strategies􏰍 If it is going to iterate until convergence􏰆 in general the initial values will not a􏰋ect 􏰓nal values􏰆 just the rate of convergence􏰍 But we can sp eed up convergence by cho osing a go o d initial assignment􏰍 We b elieve that careful choice of the initial assignment and a small 􏰓nite numb er of iterations may result in excellent or improved p erformance􏰍
Memory is allo cated for the weights for every page􏰍 Since we use single precision 􏰔oating p oint values at 􏰏 bytes each􏰆 this amounts to 􏰎􏰟􏰟 megabytes for our 􏰒􏰐 million URLs􏰍 If insu􏰌cient RAM is available to hold all the weights􏰆 multiple passes can b e made 􏰚our implementation uses half as much memory and two passes􏰛􏰍 The weights from the current time step are kept in memory􏰆 and the previous weights are accessed linearly on disk􏰍 Also􏰆 all the access to the link database􏰆 A􏰆 is linear b ecause it is sorted􏰍 Therefore􏰆 A can b e kept on disk as well􏰍 Although these data structures are very large􏰆 linear disk access allows each iteration to b e completed in ab out 􏰑 minutes on a typical workstation􏰍 After the weights have converged􏰆 we add the dangling links back in and recompute the rankings􏰍 Note after adding the dangling links back in􏰆 we need to iterate as many times as was required to remove the dangling links􏰍 Otherwise􏰆 some of the dangling links will have a zero weight􏰍 This whole pro cess takes ab out 􏰓ve hours in the current implementation􏰍 With less strict convergence criteria􏰆 and more optimization􏰆 the calculation could b e much faster􏰍 Or􏰆 more e􏰌cient techniques for estimating eigenvectors could b e used to improve p erformance􏰍 However􏰆 it should b e noted that the cost required to compute the PageRank is insigni􏰓cant compared to the cost required to build a full text index􏰍
􏰏 Convergence Prop erties
As can b e seen from the graph in Figure 􏰏 PageRank on a large 􏰎􏰈􏰈 million link database converges to a reasonable tolerance in roughly 􏰐􏰈 iterations􏰍 The convergence on half the data takes roughly 􏰏􏰐 iterations􏰍 This graph suggests that PageRank will scale very well even for extremely large collections as the scaling factor is roughly linear in log n􏰍
One of the interesting rami􏰓cations of the fact that the PageRank calculation converges rapidly is that the web is an expander􏰝like graph􏰍 To understand this b etter􏰆 we give a brief overview of
the theory of random walks on graphs􏰠 refer to Motwani􏰝Raghavan 􏰣MR􏰊􏰐 􏰥 for further
random walk on a graph is a sto chastic pro cess where at any given time step we are at a
no de of the graph and cho ose an outedge uniformly at random to determine the no de to visit at
the next time step􏰍 A graph is said to b e an expander if it is the case that every 􏰚not
subset of no des S has a neighb orho o d 􏰚set of vertices accessible via outedges emanating from no des
􏰒
details􏰍 A particular
to o large􏰛

in S 􏰛 that is larger than some
case that a graph has a go o d
larger than the second􏰝largest
if it quickly 􏰚time logarithmic in the size of the graph􏰛 converges to a limiting distribution on the set of no des in the graph􏰍 It is also the case that a random walk is rapidly􏰝mixing on a graph if and only if the graph is an expander or has an eigenvalue separation􏰍
saying factor􏰍 future in
has a go o d expansion
100000000
10000000
1000000
100000
10000
1000
100
10
factor 􏰋 times jS j􏰠 here􏰆 􏰋 is called the expansion factor􏰍 It is the expansion factor if and only if the largest eigenvalue is su􏰌ciently eigenvalue􏰍 A random walk on a graph is said to b e rapidly􏰝mixing
To relate all this to the PageRank computation􏰆 note that it is essentially the determination of the limiting distribution of a random walk on the Web graph􏰍 The imp ortance ranking of a no de is essentially the limiting probability that the random walk will b e at that no de after a su􏰌ciently large time􏰍 The fact that the PageRank computation terminates in logarithmic time is equivalent to
that the random walk is rapidly mixing or that the underlying graph
Expander graphs have many desirable prop erties that we may
b e
able to
exploit in the
computations
involving the Web graph􏰍
Convergence of PageRank Computation
0 7.5
15 22.5
30 37.5
45
52.5
Figure 􏰐􏰅 Rates of Convergence for Full Size and Half Size Link Databases
􏰐 Searching with PageRank
A ma jor application of PageRank is searching􏰍 We have implemented two search engines which use PageRank􏰍 The 􏰓rst one we will discuss is a simple title􏰝based search engine􏰍 The second search engine is a full text search engine called Go ogle 􏰣BP􏰥􏰍 Go ogle utilizes a numb er of factors to rank search results including standard IR measures􏰆 proximity􏰆 anchor text 􏰚text of links p ointing to web pages􏰛􏰆 and PageRank􏰍 While a comprehensive user study of the b ene􏰓ts of PageRank is b eyond the scop e of this pap er􏰆 we have p erformed some comparative exp eriments and provide some sample results in this pap er􏰍
􏰉
Number of Iterations
322 Million Links 161 Million Links
Total Difference from Previous Iteration

The b ene􏰓ts of PageRank are the greatest for undersp eci􏰓ed queries􏰍 For example􏰆 a query for 􏰤Stanford University􏰗 may return any numb er of web pages which mention Stanford 􏰚such as publication lists􏰛 on a conventional search engine􏰆 but using PageRank􏰆 the university home page is listed 􏰓rst􏰍
􏰐􏰍􏰇 Title Search
To test the usefulness of PageRank for search we implemented a search engine that used only the titles of 􏰇􏰑 million web pages􏰍 To answer a query􏰆 the search engine 􏰓nds all the web pages whose titles contain all of the query words􏰍 Then it sorts the results by PageRank􏰍 This search engine is very simple and cheap to implement􏰍 In informal tests􏰆 it worked remarkably well􏰍 As can b e seen in Figure 􏰑􏰆 a search for 􏰤University􏰗 yields a list of top universities􏰍 This 􏰓gure shows our MultiQuery system which allows a user to query two search engines at the same time􏰍 The search engine on the left is our PageRank based title search engine􏰍 The bar graphs and p ercentages shown are a log of the actual PageRank with the top page normalized to 􏰇􏰟􏰟􏰘􏰆 not a p ercentile which is used everywhere else in this pap er􏰍 The search engine on the right is Altavista􏰍 You can see that Altavista returns random lo oking web pages that match the query 􏰤University􏰗 and are the ro ot page of the server 􏰚Altavista seems to b e using URL length as a quality heuristic􏰛􏰍
Figure 􏰑􏰅 Comparison of Query for 􏰤University􏰗
􏰊

Web Page
Download Netscap e Software http􏰅􏰞􏰞www􏰍w􏰎􏰍org􏰞 Welcome to Netscap e
Point􏰅 It􏰙s What You􏰙re Web􏰝Counter Home Page The Blue Ribb on Campaign CERN Welcome
Yaho o􏰖
Welcome to Netscap e
Wusage 􏰏􏰍􏰇􏰅
The World Wide Web Lycos􏰆 Inc􏰍 Home Page Starting Point
Welcome to Magellan􏰖 Oracle Corp oration
Table
􏰐􏰍􏰈 Rank Merging
The reason that the title based PageRank system works so well is that the title match ensures high precision􏰆 and the PageRank ensures high quality􏰍 When matching a query like 􏰤University􏰗 on the web􏰆 recall is not very imp ortant b ecause there is far more than a user can lo ok at􏰍 For more sp eci􏰓c searches where recall is more imp ortant􏰆 the traditional information retrieval scores over full􏰝text and the PageRank should b e combined􏰍 Our Go ogle system do es this typ e of rank merging􏰍 Rank merging is known to b e a very di􏰌cult problem􏰆 and we need to sp end considerable
A Usage
System 􏰚W􏰎C􏰛
Servers
Searching
for
Statistics Consortium
Free
Sp
Web
􏰇􏰅
Top
􏰇􏰐
Page
Ranks􏰅
July
􏰇􏰊􏰊􏰑
For Online
For
additional e􏰋ort b efore However􏰆 we do b elieve
􏰐􏰍􏰎 Some Sample
the highest PageRank􏰍
􏰐􏰍􏰏 Common Case
One of the design goals of PageRank was to handle the common case for queries well􏰍 For example􏰆 a user searched for 􏰤wolverine􏰗􏰆 rememb ering that the University of Michigan system used for all administrative functions by students was called something with a wolverine in it􏰍 Our PageRank based title search system returned the answer 􏰤Wolverine Access􏰗 as the 􏰓rst result􏰍 This is sensible since all the students regularly use the Wolverine Access system􏰆 and a random user is quite likely to b e lo oking for it given the query 􏰤wolverine􏰗􏰍 The fact that the Wolverine Access site is a go o d common case is not contained in the HTML of the page􏰍 Even if there were a way of de􏰓ning go o d
we will b e able to do a reasonable evaluation of these typ es of queries􏰍 that using PageRank as a factor in these queries is quite b ene􏰓cial􏰍
Results
considerably with Go ogle􏰆 a full􏰝text search engine which uses PageRank􏰍 study is b eyond the scop e of this pap er􏰆 we provide a sample query in
queries􏰆 we encourage the reader to test Go ogle themselves 􏰣BP􏰥􏰍
Table 􏰇 shows the top 􏰇􏰐 pages based on PageRank􏰍 This particular listing was generated in July 􏰇􏰊􏰊􏰑􏰍 In a more recent calculation of PageRank􏰆 Microsoft has just edged out Netscap e for
We have exp erimented While a full􏰝scale user App endix A􏰍 For more
􏰇􏰟
eech
PageRank
􏰚average is 􏰇􏰍􏰟􏰛 􏰇􏰇􏰐􏰉􏰊􏰍􏰟􏰟 􏰇􏰟􏰒􏰇􏰒􏰍􏰒􏰟 􏰉􏰑􏰒􏰎􏰍􏰐􏰇 􏰒􏰊􏰎􏰟􏰍􏰊􏰈 􏰒􏰈􏰐􏰏􏰍􏰊􏰒 􏰒􏰟􏰇􏰟􏰍􏰎􏰊 􏰑􏰐􏰑􏰈􏰍􏰏􏰊 􏰑􏰐􏰑􏰇􏰍􏰉􏰟 􏰑􏰈􏰟􏰎􏰍􏰏􏰒 􏰐􏰊􏰑􏰎􏰍􏰈􏰒 􏰐􏰑􏰒􏰈􏰍􏰈􏰇 􏰏􏰑􏰉􏰎􏰍􏰎􏰇 􏰏􏰐􏰟􏰇􏰍􏰊􏰉 􏰎􏰉􏰑􏰑􏰍􏰉􏰈 􏰎􏰐􏰉􏰒􏰍􏰑􏰎

meta􏰝information of this form within a page􏰆 it would be problematic since a page author could not b e trusted with this kind of evaluation􏰍 Many web page authors would simply claim that their pages were all the b est and most used on the web􏰍
It is imp ortant
ab out wolverines is a very
interesting system 􏰣Mar􏰊􏰒 􏰥 that attempts to 􏰓nd sites that discuss a topic in detail by propagating the textual matching score through the link structure of the web􏰍 It then tries to return the page on the most central path􏰍 This results in go o d results for queries like 􏰤􏰔ower􏰗􏰠 the system will return go o d navigation pages from sites that deal with the topic of 􏰔owers in detail􏰍 Contrast that with the common case approach which might simply return a commonly used commercial site that had little information except how to buy 􏰔owers􏰍 It is our opinion that b oth of these tasks are imp ortant􏰆 and a general purp ose web search engine should return results which ful􏰓ll the needs of b oth of these tasks automatically􏰍 In this pap er􏰆 we are concentrating only on the common case approach􏰍
􏰐􏰍􏰐 Sub comp onents of Common Case
It is instructive to consider what kind of common case scenarios PageRank can help represent􏰍 Besides a page which has a high usage􏰆 like the Wolverine Access cite􏰆 PageRank can also represent a collab orative notion of authority or trust􏰍 For example􏰆 a user might prefer a news story simply
or authoritative source􏰆 imp ortance seems to 􏰓t
􏰑 Personalized
it is more likely to within this kind of
PageRank
b e trustworthy or authoritative􏰍 Similarly􏰆 quality or circular de􏰓nition􏰍
to note
that the goal of 􏰓nding a site that contains a great deal of di􏰋erent task than 􏰓nding the common case wolverine site􏰍
information There is an
b ecause it is linked is linked directly from the New York Times home
will receive quite a high PageRank simply b ecause it is mentioned by
seems to capture a kind of collab orative trust􏰆 since if a page was mentioned by a trustworthy
An imp ortant comp onent of the PageRank
is used as a source of rank to make up for the rank sinks such as cycles with no outedges 􏰚see Section 􏰈􏰍􏰏􏰛􏰍 However􏰆 aside from solving the problem of rank sinks􏰆 E turns out to b e a p owerful parameter to adjust the page ranks􏰍 Intuitively the E vector corresp onds to the distribution of web
pages that a random surfer general views of the Web or
p erio dically jumps to􏰍 As we see b elow􏰆 it can b e used to views which are fo cussed and p ersonalized to a particular
give broad individual􏰍
page􏰍 Of course such a story a very imp ortant page􏰍 This
calculation is E 􏰦 a vector over the Web pages which
We have p erformed most exp eriments with an E vector that is uniform over all web jjE jj􏰇 􏰡 􏰟􏰅􏰇􏰐􏰍 This corresp onds to a random surfer p erio dically jumping to a random
This is a very
Although this
Web pages with many related links receive an overly high ranking􏰍 Examples of these include copyright warnings􏰆 disclaimers􏰆 and highly interlinked mailing list archives􏰍
the Netscap e home page􏰆 we
attempt to generate default home page􏰍
who has Netscap e
want to calculate page ranks contextual information based
demo cratic choice for E since all web pages are valued simply b ecause technique has b een quite successful􏰆 there is an imp ortant problem with it􏰍
Another extreme is to have E consist entirely the Netscap e home page􏰆 and the home page of a
In b oth cases􏰆 the mailing resp ective home page got the
on the links on his home page􏰍
list problem mentioned ab ove did not o ccur􏰍 And􏰆 in b oth cases􏰆 the
highest PageRank and was followed by its immediate links􏰍 From
set as the
of a single web page􏰍 We tested two such E 􏰙s 􏰦 famous computer scientist􏰆 John McCarthy􏰍 For page ranks from the p ersp ective of a novice user In the case of John McCarthy􏰙s home page we from the p ersp ective of an individual who has given us considerable
􏰇􏰇
pages with web page􏰍 they exist􏰍
Some

Web Page
Title
John McCarthy􏰙s
John Mitchell 􏰚Stanford
Venture Law 􏰚Lo cal Startup
Stanford CS Home Page
University of Michigan AI Lab University of Toronto CS Department Stanford CS Theory Group Leadershap e Institute
John McCarthy􏰙s View Netscap e􏰙s View
Table 􏰈􏰅 Page
for
Two
Di􏰋erent
Views􏰅
McCarthy
CS
Ranks
Group􏰛 Firm􏰛
Theory
Law
􏰊􏰊􏰍􏰊􏰏􏰘 􏰇􏰟􏰟􏰍􏰟􏰟􏰘 􏰊􏰊􏰍􏰊􏰐􏰘 􏰊􏰊􏰍􏰊􏰊􏰘 􏰊􏰊􏰍􏰊􏰊􏰘 􏰊􏰐􏰍􏰊􏰑􏰘
Netscap e vs􏰍 John
that p oint􏰆 the disparity
an assortment of di􏰋erent pages􏰍 Pages related to computer science have a higher McCarthy􏰝rank than Netscap e􏰝rank and pages related to computer science at Stanford have a considerably higher McCarthy􏰝rank􏰍 For example􏰆 the Web page of another Stanford Computer Science Dept􏰍 faculty memb er is more than six p ercentile p oints higher on the McCarthy􏰝rank􏰍 Note that the page ranks are displayed as p ercentiles􏰍 This has the e􏰋ect of compressing large di􏰋erences in PageRank at the top of the range􏰍
Such p ersonalized page ranks may have a numb er of applications􏰆 including p ersonal search engines􏰍 These search engines could save users a great deal of trouble by e􏰌ciently guessing a large part of their interests given simple input such as their b o okmarks or home page􏰍 We show an
PageRank
Percentile PageRank 􏰇􏰟􏰟􏰍􏰟􏰟􏰘
􏰇􏰟􏰟􏰍􏰟􏰟􏰘
Percentile 􏰊􏰊􏰍􏰈􏰎􏰘 􏰊􏰎􏰍􏰉􏰊􏰘 􏰊􏰊􏰍􏰉􏰈􏰘 􏰊􏰊􏰍􏰉􏰎􏰘 􏰊􏰊􏰍􏰊􏰏􏰘 􏰊􏰊􏰍􏰟􏰊􏰘 􏰊􏰊􏰍􏰟􏰐􏰘 􏰊􏰒􏰍􏰇􏰟􏰘
Home
Page
decreased􏰍 In
Table 􏰈􏰆
we show the resulting page rank p ercentiles for
example of this in App endix A with the
while there are many p eople on the web
of a colleague of John McCarthy named John Mitchell􏰍
􏰤Mitchell􏰗 query􏰍 In this example􏰆 we demonstrate that named Mitchell􏰆 the numb er one result is the home page
􏰑􏰍􏰇 Manipulation by Commercial Interests
These typ es of p ersonalized PageRanks are virtually immune to manipulation by commercial in􏰝
terests􏰍 For a
non􏰝imp ortant
advertisements􏰚links􏰛 on imp ortant
This immunity to manipulation is an extremely imp ortant prop erty􏰍 This kind of commercial ma􏰝 nipulation is causing search engines a great deal of trouble􏰆 and making features that would b e great to have very di􏰌cult to implement􏰍 For example fast up dating of do cuments is a very desirable feature􏰆 but it is abused by p eople who want to manipulate the results of the search engine􏰍
A compromise b etween the two extremes of uniform E and
all the ro ot level pages of all web servers􏰍 Notice this will allow
Someone who wished to manipulate this system could simply create a large numb er of ro ot level servers all p ointing at a particular site􏰍
page to get a high pages to link to it􏰍
PageRank􏰆 it must convince an imp ortant page􏰆 or a lot of At worst􏰆 you can have manipulation in the form of buying sites􏰍 But􏰆 this seems well under control since it costs money􏰍
􏰇􏰈
single page E is to let E consist of
some manipulation of
PageRanks􏰍

􏰒 Applications
􏰒􏰍􏰇 Estimating Web Tra􏰌c
Because PageRank roughly corresp onds to a random web surfer 􏰚see Section 􏰈􏰍􏰐􏰛􏰆 it is interesting to see how PageRank corresp onds to actual usage􏰍 We used the counts of web page accesses from NLANR 􏰣NLA 􏰥 proxy cache and compared these to PageRank􏰍 The NLANR data was from several national proxy caches over the p erio d of several months and consisted of 􏰇􏰇􏰆􏰉􏰇􏰒􏰆􏰑􏰑􏰐 unique URLs with the highest hit count going to Altavista with 􏰑􏰎􏰉􏰆􏰑􏰐􏰒 hits􏰍 There were 􏰈􏰍􏰑 million pages in the intersection of the cache data and our 􏰒􏰐 million URL database􏰍 It is extremely di􏰌cult to compare these datasets analytically for a numb er of di􏰋erent reasons􏰍 Many of the URLs in the cache access data are p eople reading their p ersonal mail on free email services􏰍 Duplicate server names and page names are a serious problem􏰍 Incompleteness and bias a problem is b oth the PageRank data and the usage data􏰍 However􏰆 we did see some interesting trends in the data􏰍 There seems to b e a high
usage of p ornographic sites in the cache data􏰆 but these sites generally had
b elieve this is b ecause p eople do not want to link to p ornographic sites from
Using this technique of lo oking for di􏰋erences b etween PageRank and usage􏰆
􏰓nd things that p eople like to lo ok at􏰆 but do not want to mention on their web pages􏰍 There are some sites that have a very high usage􏰆 but low PageRank such as netscap e􏰍yaho o􏰍com􏰍 We b elieve there is probably an imp ortant backlink which simply is omitted from our database 􏰚we only have a partial link structure of the web􏰛􏰍 It may b e p ossible to use usage data as a start vector for PageRank􏰆 and then iterate PageRank a few times􏰍 This might allow 􏰓lling in holes in the usage data􏰍 In any case􏰆 these typ es of comparisons are an interesting topic for future study􏰍
􏰒􏰍􏰈 PageRank as Backlink Predictor
One justi􏰓cation for PageRank is that it is a predictor for backlinks􏰍 In 􏰣CGMP􏰊􏰉 􏰥 we explore the
issue of how to crawl the web e􏰌ciently􏰆
of the Stanford web that PageRank is a b etter predictor of future citation counts than citation counts themselves􏰍
The exp eriment assumes that the system starts out with only a single URL and no other information􏰆 and the goal is to try to crawl the pages in as close to the optimal order as p ossible􏰍 The optimal order is to crawl pages in exactly the order of their rank according to an evaluation function􏰍 For the purp oses here􏰆 the evaluation function is simply the numb er of citations􏰆 given complete information􏰍 The catch is that all the information to calculate the evaluation function is
trying to crawl b etter do cuments 􏰓rst􏰍 We found on tests
not available until after all the do cuments have
data􏰆 PageRank is a more e􏰋ective way to order
In other words􏰆 PageRank is a b etter predictor
the numb er of citations􏰖 The explanation for this seems to b e that PageRank avoids the lo cal maxima that citation counting gets stuck in􏰍 For example􏰆 citation counting tends to get stuck in
lo cal collections like the Stanford CS
cited pages in other areas􏰍 PageRank
preference to its children resulting in an e􏰌cient􏰆 broad search􏰍
b een crawled􏰍 It turns out using the incomplete the crawling than the numb er of known citations􏰍 than citation counting even when the measure is
web pages􏰆 taking a long time to branch out and 􏰓nd highly quickly 􏰓nds the Stanford homepage is imp ortant􏰆 and gives
This ability of PageRank to predict citation counts is a p owerful justi􏰓cation for using PageR􏰝 ank􏰍 Since it is very di􏰌cult to map the citation structure of the web completely􏰆 PageRank may even b e a b etter citation count approximation than citation counts themselves􏰍
􏰇􏰎
low PageRanks􏰍 We their own web pages􏰍
it may b e
p ossible to

Figure 􏰒􏰅 PageRank Proxy
􏰒􏰍􏰎 User Navigation􏰅 The PageRank Proxy
We have develop ed a web proxy application that annotates each link that a user sees with its PageRank􏰍 This is quite useful􏰆 b ecause users receive some information ab out the link b efore they click on it􏰍 In Figure 􏰒 is a screen shot from the proxy􏰍 The length of the red bars is the log of the URL􏰙s PageRank􏰍 We can see that ma jor organizations􏰆 like Stanford University􏰆 receive a very high ranking followed by research groups􏰆 and then p eople􏰆 with professors at the high end of the p eople scale􏰍 Also notice ACM has a very high PageRank􏰆 but not as high as Stanford University􏰍 Interestingly􏰆 this PageRank annotated view of the page makes an incorrect URL for one of the
professors glaringly obvious since the professor has a embarrassingly low
this to ol seems useful
lo oking at the results
Yaho o􏰙s listings􏰍 The
b e interesting􏰍 Or􏰆 if the user has some idea where the link they
􏰤imp ortance􏰗 sp ectrum􏰆 they should b e able to scan for it much more quickly using the proxy􏰍
for authoring pages as well as navigation􏰍 This from other search engines􏰆 and pages with large
proxy can help users decide which links in
a long
􏰇􏰏
PageRank􏰍 Consequently proxy is very helpful for numb ers of links such as listing are more likely to are lo oking for should fall in the

􏰒􏰍􏰏 Other Uses of PageRank
The original goal of PageRank was a way to sort backlinks so if there were a large numb er of backlinks for a do cument􏰆 the 􏰤b est􏰗 backlinks could b e displayed 􏰓rst􏰍 We have implemented such a system􏰍 It turns out this view of the backlinks ordered by PageRank can b e very interesting when trying to understand your comp etition􏰍 For example􏰆 the p eople who run a news site always want to keep track of any signi􏰓cant backlinks the comp etition has managed to get􏰍 Also􏰆 PageRank can
help the user decide if a site is trustworthy or not􏰍 For example􏰆 a user might information that is directly cited from the Stanford homepage􏰍
􏰉 Conclusion
b e
inclined to trust
the World Wide pages􏰆 regardless
In this pap er􏰆 we have taken on the audacious task of condensing every page Web into a single numb er􏰆 its PageRank􏰍 PageRank is a global ranking of all web of their content􏰆 based solely on their lo cation in the Web􏰙s graph structure􏰍
Using PageRank􏰆 we are able to order search results so that more imp ortant and central Web pages are given preference􏰍 In exp eriments􏰆 this turns out to provide higher quality search results to users􏰍 Th intuition b ehind PageRank is that it uses information which is exernal to the Web pages themselves 􏰝 their backlinks􏰆 which provide a kind of p eer review􏰍 Furthermore􏰆 backlinks from 􏰤imp ortant􏰗 pages are more signi􏰓cant than backlinks from average pages􏰍 This is encompassed in the recursive de􏰓nition of PageRank 􏰚Section 􏰈􏰍􏰏􏰛􏰍
PageRank could b e used to separate out a small set of commonly used do cuments which can answer most queries􏰍 The full database only needs to be consulted when the small database is not adequate to answer a query􏰍 Finally􏰆 PageRank may b e a go o d way to help 􏰓nd representative pages to display for a cluster center􏰍
We have found a numb er of applications for PageRank in addition to search which include tra􏰌c estimation􏰆 and user navigation􏰍 Also􏰆 we can generate p ersonalized PageRanks which can create a view of Web from a particular p ersp ective􏰍
Overall􏰆 our exp eriments with PageRank suggest that the structure of the Web graph is very useful for a variety of information retrieval tasks􏰍
References
􏰣BP􏰥 Sergey Brin and Larry Page􏰍 Go ogle search engine􏰍 http􏰅􏰞􏰞google􏰍stanford􏰍edu􏰍
􏰣CGMP􏰊􏰉􏰥 Jungho o Cho􏰆 Hector Garcia􏰝Molina􏰆 and Lawrence Page􏰍 E􏰌cient crawling through url ordering􏰍 In To Appear􏰅 Proceedings of the Seventh International Web Conference 􏰚WWW 􏰊􏰉􏰛􏰆 􏰇􏰊􏰊􏰉􏰍
􏰣Gar􏰊􏰐􏰥 Eugene Gar􏰓eld􏰍 New international professional so ciety signals the maturing of sciento􏰝 metrics and informetrics􏰍 The Scientist􏰆 􏰊􏰚􏰇􏰑􏰛􏰆 Aug 􏰇􏰊􏰊􏰐􏰍 http􏰅􏰞􏰞www􏰍the􏰝 scientist􏰍 library􏰍upenn􏰍edu􏰞yr􏰇􏰊􏰊􏰐􏰞august􏰞issi􏰬􏰊􏰐􏰟􏰉􏰈􏰇􏰍ht􏰘ml􏰍
􏰣Gof􏰒􏰇􏰥 William Go􏰋man􏰍 A mathematical metho d for analyzing the growth of a scienti􏰓c discipline􏰍 Journal of the ACM􏰆 􏰇􏰉􏰚􏰈􏰛􏰅􏰇􏰒􏰎􏰦􏰇􏰉􏰐􏰆 April 􏰇􏰊􏰒􏰇􏰍
􏰣Kle􏰊􏰉􏰥 Jon Kleinb erg􏰍 Authoritative sources in a hyp erlinked environment􏰍 In Proceedings of the Nineth Annual ACM􏰝SIAM Symposium on Discrete Algorithms􏰍􏰆 􏰇􏰊􏰊􏰉􏰍
􏰇􏰐
on

􏰣Mar􏰊􏰒􏰥
􏰣MF􏰊􏰐􏰥
􏰣MFH􏰊􏰐􏰥
􏰣MR􏰊􏰐􏰥
􏰣NLA􏰥
􏰣PB􏰊􏰉􏰥
􏰣Pit􏰊􏰒􏰥
􏰣PPR􏰊􏰑􏰥
􏰣San􏰊􏰐􏰥
􏰣Sp e􏰊􏰒􏰥
􏰣Til􏰥
􏰣WVS􏰜 􏰊􏰑􏰥
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Visualizing complex hyp er􏰝
Sougata Mukherjea􏰆 James D􏰍 Foley􏰆 and Scott Hudson􏰍
media networks through multiple hierarchical views􏰍 In Proceedings Conference on Human Factors in Computing Systems􏰆 volume 􏰇 of Visualizations􏰆 pages 􏰎􏰎􏰇􏰦􏰎􏰎􏰒􏰆 􏰇􏰊􏰊􏰐􏰍
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University Press􏰆
R􏰍 Motwani and P􏰍 Raghavan􏰍 Randomized Algorithms􏰍 Cambridge 􏰇􏰊􏰊􏰐􏰍
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Lawrence Page and Sergey Brin􏰍 The anatomy of a large􏰝scale hyp ertextual web search engine􏰍 In To Appear􏰅 Proceedings of the Seventh International Web Conference 􏰚WWW 􏰊􏰉􏰛􏰆 􏰇􏰊􏰊􏰉􏰍
James E􏰍 Pitkow􏰍 Characterizing World Wide Web Ecologies􏰍 PhD thesis􏰆 Georgia Institue of Technology􏰆 June 􏰇􏰊􏰊􏰒􏰍
Peter Pirolli􏰆 James Pitkow􏰆 and Ramana Rao􏰍 Silk from a sow􏰙s ear􏰅 Extracting usable structure from the web􏰍 In Michael J􏰍 Taub er􏰆 Victoria Bellotti􏰆 Robin Je􏰋ries􏰆 Jo ck D􏰍 Mackinlay􏰆 and Jakob Nielsen􏰆 editors􏰆 Proceedings of the Conference on Human Factors in Computing Systems 􏰅 Common Ground􏰆 pages 􏰇􏰇􏰉􏰦􏰇􏰈􏰐􏰆 New York􏰆 􏰇􏰎􏰦􏰇􏰉 April 􏰇􏰊􏰊􏰑􏰍 ACM Press􏰍
Neera ja Sankaran􏰍 Sp eculation in the biomedical community ab ounds over likely candi􏰝 dates for nob el􏰍 The Scientist􏰆 􏰊􏰚􏰇􏰊􏰛􏰆 Oct 􏰇􏰊􏰊􏰐􏰍 http􏰅􏰞􏰞www􏰍the􏰝 scientist􏰍library􏰍 upenn􏰍edu􏰞yr􏰇􏰊􏰊􏰐􏰞oct􏰞nobel􏰬􏰊􏰐􏰇􏰟􏰟􏰈􏰍html􏰘􏰍
Ellen Sp ertus􏰍 Parasite􏰅 Mining structural information on the web􏰍 In Proceedings of the Sixth International WWW Conference􏰆 Santa Claram USA􏰆 April􏰆 􏰇􏰊􏰊􏰒􏰆 􏰇􏰊􏰊􏰒􏰍 http􏰅􏰞􏰞www􏰑􏰍nttlabs􏰍com􏰞HyperNews􏰞get􏰞PAPER􏰈􏰟􏰑􏰍html􏰍
Hop e N􏰍 Tillman􏰍 Evaluating quality on the net􏰍 http􏰅􏰞􏰞www􏰍tiac􏰍net􏰞users􏰞hope􏰞 findqual􏰍html􏰍
Ron Weiss􏰆 Bienvenido V􏰕elez􏰆 Mark A􏰍 Sheldon􏰆 Chanathip Manprempre􏰆 Peter Szi􏰝 lagyi􏰆 Andrzej Duda􏰆 and David K􏰍 Gi􏰋ord􏰍 HyPursuit􏰅 A hierarchical network search engine that exploits content􏰝link hyp ertext clustering􏰍 In Proceedings of the 􏰒th ACM Conference on Hypertext􏰆 pages 􏰇􏰉􏰟􏰦􏰇􏰊􏰎􏰆 New York􏰆 􏰇􏰑􏰦􏰈􏰟 March 􏰇􏰊􏰊􏰑􏰍 ACM Press􏰍
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App endix A
This App endix contains several sample query results using Go ogle􏰍 The 􏰓rst is a query for 􏰤Digital Libraries􏰗 using a uniform E􏰍 The next is a query for 􏰤Mitchell􏰗 using E consisting just of John McCarthy􏰙s home page􏰍
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