RANKING ALGORITHMS FOR WEB SITES
Finding Authoritative Academic Web Sites and Researchers
Dalibor Fiala, François Rousselot
INSA Strasbourg, 24 bd de la Victoire, 67084 Strasbourg, France
Karel Jezek
Dept. of Comp. Science and Engineering, University of West Bohemia, Plzen, Czech Republic
Keywords: Authorities, citation analysis, prestige, ranking algorithms, Web mining.
Abstract: In this paper, we discuss several common ranking algorithms for Web pages and we present a methodology
based on them for finding authoritative researchers by analyzing academic Web sites. We show a case study
in which we concentrate on a set of French computer science departments’ Web sites. We analyze the
relations between them via hyperlinks and find the most important ones. We then examine the contents of
the research papers present on these sites and determine the most authoritative French authors. We also
propose some future improvements.
1 INTRODUCTION
Notions of importance, significance, authority,
prestige, quality and other synonyms play a major
role in social networks of all types. They denote an
object that has a large impact on the other objects in
the community. Perhaps the best example are
bibliographic citations in the scientific literature.
This kind of analysis has become essential in the
Web domain as well.
In the Web domain, citations are links among
Web pages or Web sites (when we talk about site
level). Therefore, current Web search engines make
use of various link-based quality ranking algorithms
whose ranking they combine with the keyword
search results to offer the user not only topic-
relevant but also high quality Web pages. These
algorithms may be recursive such as PageRank or
HITS (Chakrabarti, 2002) or simple like In-Degree
which just counts in-links. Some studies have shown
that all three measures are strongly positively
correlated (Ding, 2002). There exist many
modifications, e.g. PageRank for bibliographic
citations (Sidiripoulos, 2005). Closest to our work is
the research in (Thelwall, 2003), but in addition to
the relations between Web sites we also studied the
contents of the documents found on them. Other
authors have tried to determine the importance of
Web sites of Universities rather than departments as
we have done. (See http://www.webometrics.info.)
2 EXPERIMENTS
In this section, we will describe our experiment with
Web sites of French computer science departments.
Even though we limited our experiments by topic
and scope, the methodology we used was
sufficiently general to be able of applying to a
completely different scientific field. First, we had to
draw up a list of laboratories. To do this, we looked
up in Web directories and we also submitted queries
to Web search engines. From these Web pages, we
manually selected 80 final sites that constituted our
set of departments. The first goal was to determine
the most authoritative sites as of May 2006.
2.1 Authoritative Institutions
To accelerate the process of creating the Web graph,
we did not make use of a Web spider of our own, but
we took advantage of a service provided by the
search engine Yahoo! We submitted to it queries in
this form: site:www.loria.fr linkdomain:www.irisa.fr
372
Fiala D., Rousselot F. and Jezek K. (2007).
RANKING ALGORITHMS FOR WEB SITES - Finding Authoritative Academic Web Sites and Researchers.
In Proceedings of the Third International Conference on Web Information Systems and Technologies - Web Interfaces and Applications, pages 372-375
DOI: 10.5220/0001263503720375
Copyright
c
 SciTePress

which returns the number of documents on
www.loria.fr containing at least one link to
documents on www.irisa.fr. For us, it is a weight of
the edge from www.loria.fr to www.irisa.fr. We had
to construct 6 320 queries in this way. Of course, the
construction and submission of queries, storing of
results, and the graph creation were automated. (The
figure of the Web graph with 393 edges is available
at http://home.zcu.cz/~dalfia/papers/France.svg.)
The drawbacks of relying solely upon search
engines are discussed a great deal in (Thelwall,
2003). The problem consists primarily in
“instability” of the results. This means that results
obtained one day differ from those of another one.
Another disadvantage is that the results are not
transparent. We do not know which document
formats are taken into account, how duplicate
documents are treated, etc.
2.1.1 Results and Discussion
We applied three ranking methods to the Web graph
of 80 sites of choice. First, we computed in-degrees
of the nodes in the citation graph without respect to
edge weights (i.e. each edge has a weight of one).
Then, we computed HITS authorities for the graph
nodes and, finally, we generated PageRanks
(HostRanks, in fact) for all of the nodes. We can see
the results in tables 1 and 2. The sites are sorted by
in-links (citations), i.e. by the total number of links
to this site from other sites in the set (with some
limitations imposed by the search engine). The first
place belongs to www-futurs.inria.fr, whose
positions achieved by the other methods, though, are
much worse. We can suppose that the reason for this
is a very strong support from a particular site. (After
inspecting the Web graph, we can see that it is
www.lifl.fr.) The following sites always have high
ranks - www-sop.inria.fr, www.loria.fr, www.lri.fr.
We can surely consider them as authoritative.
Of course, the number of in-links often depends
on the number of documents on the target site. Their
numbers vary greatly due to different sizes of
hosting institutions, existence of server aliases,
preference of various document formats and
document generation (dynamic Web pages), etc.
One way of tackling this problem is to normalize the
number of citations somehow. For instance, it is
possible to divide the number of citations by the
number of documents on a particular site or by the
number of staff of the corresponding institution
(Thelwall, 2003).
The phase of finding significant institutions
enables us to reduce the set of Web sites that we are
going to analyze in the next stage. For example, we
might discard the last eight sites in Table 2, i.e. the
least important sites. However, our case study
(French academic computer science Web sites) has a
sufficiently small data set so that no reduction is
necessary. Measuring the quality of academic
institutions with webometric tools is justified in
(Thelwall, 2003), where Web-based rankings
correlated with official rankings.
2.2 Authoritative Researchers
In addition to studying links in a collection of
computer science Web sites, we were also interested
in the documents themselves found on these Web
sites. Thus, we downloaded potential research
papers from the sites in question. In practice, that
meant collecting PDF and PostScript files because
most research publications publicly accessible on the
Web are in these two formats. First, we had to
preprocess our download corpus. We unpacked
archives and converted observed files to plain text
via external utilities. So, at the beginning, we had
about 45 thousand potential research papers. We
discarded duplicates and examined the remaining
documents. We used a simple rule to categorize the
documents. In case they included some kind of
references section they were considered as papers. In
this way, we obtained some 16 000 papers in the
end, i.e. over thirty thousand documents did not look
like research articles.
2.2.1 Information Extraction
The next task is to extract information from the
papers needed for citation analysis, i.e. names of
authors, titles of papers, etc. We employ the same
methodology with use of Hidden Markov Models
(HMM) as in (Seymore, 1999).
We had to construct a graph with authors
(identified by surnames and initials of their first and
middle names) as nodes and citations in publications
as edges. The final graph (without duplicate edges
and self-citations) had almost 86 000 nodes and
about 477 000 edges. Strictly said, when we talk
about surnames, we mean words identified as
surnames. Of course, many of these words were not
surnames (they were incorrectly classified) or they
were foreign surnames which we did not wish to
consider. From the citation graph with “surnames”
as graph nodes we determined the most authoritative
French authors using the three different ranking
methods. (The recognition of a French surname was
done manually.) See Table 3 for details.
RANKING ALGORITHMS FOR WEB SITES - Finding Authoritative Academic Web Sites and Researchers
373

Table 1: Ranking of French Web sites (1–40).
Site InD HITS PR
1
www-futurs.inria.fr 45
41
53
2
www-sop.inria.fr 1
1
9
3
www.loria.fr 1
5
3
4
www.lri.fr 6
6
10
5
www-rocq.inria.fr 13
12
28
6
www.irisa.fr 4
3
18
7
www.lifl.fr 5
7
4
8
www.lix.polytechnique.fr 20
17
26
9
dpt-info.u-strasbg.fr 39
53
43
10
www.inrialpes.fr 6
8
2
11
www.irit.fr 9
4
8
12
www.liafa.jussieu.fr 13
15
39
13
www.lirmm.fr 1
11
1
14
www.labri.fr 13
13
30
15
www-leibniz.imag.fr 10
14
13
16
liris.cnrs.fr 13
16
11
17
www.prism.uvsq.fr 13
25
5
18
www.di.ens.fr 34
26
44
19
www.lip6.fr 20
21
40
20
www.laas.fr 6
2
27
21
dep-info.u-psud.fr 61
58
69
22
www-lil.univ-littoral.fr 25
34
35
23
www-verimag.imag.fr 25
37
16
24
www.i3s.unice.fr 25
31
7
25
eurise.univ-st-etienne.fr 25
23
32
26
www-lsr.imag.fr 34
26
37
27
www.info.unicaen.fr 13
10
14
28
www-timc.imag.fr 12
9
17
29
www-sic.univ-poitiers.fr 45
46
50
30
cedric.cnam.fr 25
22
38
31
www.dil.univ-mrs.fr 39
54
25
32
www-lmc.imag.fr 25
29
34
33
www.info.univ-angers.fr 34
44
24
34
lifc.univ-fcomte.fr 20
32
21
35
eric.univ-lyon2.fr 10
19
6
36
www-id.imag.fr 25
33
15
37
www-lipn.univ-
p
aris13.fr 13
24
29
38
dept-info.labri.fr 25
18
36
39
www.isima.fr 39
43
48
40
sis.univ-tln.fr 20
28
12
2.2.2 Results and Discussion
The rankings produced by In-Degree and HITS are
very similar (the top five researchers are exactly the
same) whereas that by PageRank is rather different.
The authors in In-Degree and HITS are more or less
the same (only in various positions), but PageRank
introduces some new names. However, there are two
authors (“Halbwachs N” and “Berry G”) occurring
in top five of each ranking. We can certainly call
these researchers authorities.
Table 2: Ranking of French Web sites (41–80).
Site InD HITS PR
41
www-clips.imag.fr
25 30 22
42
www.lisi.ensma.fr
39 40 33
43
www-info.iutv.univ-paris13.fr
61 69 72
44
www.lif.univ-mrs.fr
34 36 31
45
www.cril.univ-artois.fr
39 35 41
46
www.li.univ-tours.fr
34 42 45
47
citi.insa-lyon.fr
45 45 54
48
deptinfo.unice.fr
39 38 46
49
msi.unilim.fr
52 55 64
50
www.iut-info.univ-lille1.fr
61 62 65
51
www.lia.univ-avignon.fr
20 20 23
52
lil.univ-littoral.fr
52 48 57
53
lisi.insa-lyon.fr
45 39 47
54
www.isc.cnrs.fr
45 71 19
55
www.if.insa-lyon.fr
61 72 52
56
sirac.inrialpes.fr
61 62 62
57
phalanstere.univ-mlv.fr
45 65 20
58
www.lalic.paris4.sorbonne.fr
45 47 61
59
www.icp.inpg.fr
52 51 49
60
www-valoria.univ-ubs.fr
52 57 51
61
lihs.univ-tlse1.fr
52 48 60
62
www.epita.fr
52 67 42
63
llaic3.u-clermont1.fr
52 51 56
64
lsiit.u-strasbg.fr
52 48 57
65
liuppa.univ-pau.fr
52 56 66
66
wwwhds.utc.fr
61 66 55
67
www.depinfo.uhp-nancy.fr
61 68 59
68
lrlweb.univ-bpclermont.fr
61 62 62
69
www-lium.univ-lemans.fr
61 70 67
70
www.dptinfo.ens-cachan.fr
61 58 68
71
www.ai.univ-paris8.fr
61 58 69
72
www.lita.univ-metz.fr
61 58 69
73
dept-info.univ-brest.fr
73 73 73
74
lina.atlanstic.net
73 73 73
75
lis.snv.jussieu.fr
73 73 73
76
psiserver.insa-rouen.fr
73 73 73
77
www.listic.univ-savoie.fr
73 73 73
78
www-info.enst-bretagne.fr
73 73 73
79
www.info.iut.u-bordeaux1.fr
73 73 79
80
www.info.iut-tlse3.fr
73 73 79
Let us underline several facts. First, we did not
disambiguate the names. Thus, a couple of authors
may actually be represented by one name. Even
adding first names does not resolve this problem.
One solution would be to cluster authors according
to their co-authors or publication topics as it is done
in (Han, 2005). Authors report that this method
works well with European (English) names but it
achieves accuracy of only 60 – 70% with Chinese
names. Second, duplicate citations are handled only
in the sense that we remove duplicate documents
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374

before analysis. We do not examine whether two or
more papers having perhaps only small differences
are one publication in reality. Their references to
another paper are counted separately.
Table 3: Authoritative French CS researchers.
In-Degree HITS PageRank
1 Halbwachs N Halbwachs N Cahon S
2 Caspi P Caspi P Berry G
3 Sifakis J Sifakis J Filiol E
4 Berry G Berry G Halbwachs N
5 Benveniste A Benveniste A Zhang Z
6 Abiteboul S Nicollin X Benveniste A
7 Maler O Cousot R Lavallée S
8 Nicollin X Raymond P Dombre E
9 Cousot P Cousot P Boudet S
10 Cousot R Abiteboul S Dégoulange E
11 Raymond P Maler O Gourdon A
12 Bouajjani A Asarin E Abiteboul S
13 Asarin E Comon H Charpin P
14 Comon H Bouajjani A Carlet C
15 Zhang Z Coupaye T Cohen G
16 Berstel J Berstel J Troccaz J
17 Meyer B David B Abdalla M
18 Florescu D Arnold A Payan Y
19 Baccelli F Pilaud D Cousot R
20 Leroy X Bruneton E David R
21 Bruneton E Maraninchi F Cousot P
22 Flajolet P Meyer B Caspi P
23 Arnold A Leroy X Sifakis J
24 Graf S Bensalem S Deransart P
25 Cohen J Graf S Maler O
26 Coupaye T Tripakis S Bouajjani A
27 Pilaud D Lakhnech Y Dubois D
28 Lakhnech Y Bozga M Caron P
29 David R Gautier T Pierrot F
30 Faugeras O Liu J Raymond P
Third, deciding whether or not a researcher is
French is inherently subjective. Our decision was
based on searching with several general and
specialized search engines. Ideally, we found the
researcher’s home page hosted by a French Web site
or affiliation to a French institution given in an
article. Of course, by French authors we also mean
those who had lived and worked in France for a long
time. We are aware that this feature is particularly
fuzzy. The name ambiguity (one author may be
known under more names and one name may
represent a couple of authors) is to be reflected in
future improvements. For all these reasons, the
actual citation numbers are less interesting than the
ranking itself. Let us not forget that the ranking is a
result of those 16 000 papers we got. The question is
how it would change if more papers were analyzed.
3 CONCLUSIONS
We present a methodology and a case study of
finding authoritative researchers on the Web. We
applied several ranking algorithms to a set of French
academic computer science Web sites and
determined the most authoritative ones.
This step normally enables reducing the volume
of data to be analyzed since we could continue
finding researchers on the more important sites only.
Further, we analyzed the research papers publicly
available on the sites and we determined the most
significant researchers by applying several ranking
techniques to the citation graph. The results we
achieved are not quite reliable due to the constraints
and problems mentioned above, but we believe that
our methodology is practical as we have shown in
our experiments. The methodology we have
developed is general, which will enable us to focus
on other areas of the Web as well.
This work was supported in part by the Ministry of
Education of the Czech Republic under Grant
2C06009.
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