COMPETITIVE INTELLIGENCE BASED ON
SOCIAL NETWORKS FOR DECISION MAKING
Fco Fe nando de la Rosa T oyano1, Ma ía Te esa Gómez López1and Ra ael
Ma ínez Gasca1
1 Depa amen o de Lenguajes y Sis emas In o má icos. Dp o. Lenguajes y sis emas
in o ma icos. Uni e si y o Se ille, Spain
{ osa , may egomez, gasca}@us.es
Abs ac . In p e ious wo ks a amewo k has been p esen ed o ex ac om
in e ne he scien i ic communi y in e es ed in a speci ic opic. The p ocess uses
sea ch engines que y esul s and e-mails add ess co-occu ences o ob ain he
in isible colleges and sub opics o a communi y. This wo k p esen s he use o
his echnique o implemen se e al compe i i e in elligence asks o help in
decision making in a esea ch a ea. In o de o show an illus a i e pu pose, his
echnique is applied o analyze he social ne wo k o pa icipan s in se e al
Veille S a égique Scien i ique & Technologique edi ions.
Keywo ds: Social ne wo ks ex ac ion, compe i i e in elligence, sea ch engine
mining, emails add ess mining.
1 In oduc ion
One o he consequences caused by in e ne expansion is he exponen ial g ow h o
public in o ma ion. This in o ma ion is s o ed in a se o in e linked he e ogeneous
sou ces. Sea ch engines play a key ole in in e ne sea ching in o ma ion, bu he
gene al app oaches o analyze he in o ma ion sys ems canno in eg a e di e en
sou ces. The analysis o he s o ed in o ma ion in a sys ema ic and au oma ic way can
help o decision making in compe i i e in elligence. In his con ex , he co-
occu ences analysis becomes impe a i e o implemen in elligence compe i i e asks.
In p e ious wo ks a p ocess o ex ac au oma ically he scien i ic communi y
in e es ed in a speci ic opic was p esen ed. The p ocess ca ies ou a sys ema ic
sea ch engines que ies. These que ies a e specially designed o ex ac he goal
ne wo k h ough a pos e io analysis o he sea ch engines eques s. Analysing his
ex ac ed in o ma ion is also possible o de e mine he communi y sub opics o
in e es . This pape discusses he use o his echnique o implemen se e al
compe i i e in elligence asks. Fo example: sea ching o expe s, ga he ing o
in o ma ion, o ganiza ions collabo a ions analysis, coun ies collabo a ions analysis
o p oduc s impac s analysis.
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The pape is di ided in o he ollowing sec ions. Sec ion 2 p esen s he ela ed wo ks
and compa es hem wi h ou p oposal. Sec ion 3 p esen s he Social ne wo ks opic-
d i en ex ac ion algo i hm. Sec ion 4 analyzes some Compe i i e In elligence Tasks.
In o de o illus a e he pu pose, he de ined p ocess is applied o ex ac he social
ne wo k o pa icipan s in he Veille S a égique Scien i ique & Technologique
(VSST). Finally, conclusions and u u e wo k a e p esen ed.
2 Rela ed Wo ks
P e ious wo ks ha e de ined social ne wo ks ex ac ion p ocesses o di e en
in o ma ion web sou ces: sea ch engines [8][10][12], cha s [14], DBLP [5], FOAF
a chi es [12][13], Sou ceFo ge [1], mailing lis s [2], e c. The e iew o his pape is
es ic ed o app oaches using sea ch engines o ex ac social ne wo ks. One o he
sys ems ha uses sea ch engines o ex ac social ne wo ks is REFERRAL WEB [8]
which was designed o use Al a is a engine, he ob ained ne wo k is ocused on a
speci ic pe son (egocen ic ne wo k). Fo his eason, i only needs o know he name
o he ego. By mean o an en i y ecogni ion sys em ex ac s a lis o ela ed people.
To measu e he signi icance o he ela ionship be ween X and Y ( he a iable Y
con ains any o he pe sons ela ed o X) uses he Jacca d coe icien [7]. The abo e
p ocess may be epea ed ecu si ely.
Recen ly wo sys ems POLYPHONET [10][11] and FLINK [12][13] we e
launched, hese sys ems ob ain he social ne wo k using a lis o he membe s o a
de e mined communi y. The basic algo i hm o bo h sys ems mus do a que y o each
pai X and Y o c ea e he a ini y ma ix (X and Y a e wo di e en names o he
lis ). Bo h sys ems use a h eshold o de e mine when ela ionships a e meaning ul.
These sys ems ha e se e al disad an ages, o example he calcula ed a ini ies a e
di icul o unde s and. The e ms co-occu ence on he indexed pages may be due o
se e al ac o s: co-au ho ship, pa icipa ion in he same e en (i.e., p og am
commi ee), e e enced in he same pape , e c. By using pe sonal names in he que y
could add ambigui y o he esul s, since he p obabili y ha he name e e s o mo e
han one pe son is high. Some imes se e al names e e ence o he same pe son (i.e.,
‘Ra ael Ma ínez Gasca’ o ‘R.M. Gasca’). Ano he disad an age is he high cos o
ex ac ing he social ne wo k, since in he wo s case o each pai o membe s a
que y mus be pe o med. This is a majo p oblem gi en ha licenses o use sea ch
engines may ha e limi a ions. Fo example, Google does no allow mo e han 1000
que ies pe day ( o calcula e a ne wo k o 500 ac o s would need 125 days). The
scalable algo i hm implemen a ions [11] ha e educed he numbe s o necessa y
que ies o comple e a whole ne wo k (acco ding o he au ho , o 503 ac o s 19,852
que ies a e needed, 20 days).
The e iewed wo ks use lis s o names o ex ac social ne wo ks. In con as he e
is he possibili y o eplacing he lis s o names by e-mails add ess. In [9] is desc ibed
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an algo i hm o ex ac ing social ela ionships o a speci ic in e ne domain using e-
mails add ess. This algo i hm is used o social enginee ing a acks in secu i y o
compu e sys ems a ea and has a high cos o ex ac ing he social ne wo k, o each
pai o e-mail add ess a que y mus be pe o med. This pape p oposes a social
ne wo k ex ac ion algo i hm based on e-mails o imp o e he complexi y o e iewed
algo i hms. The que y model ha is used makes i mo e obus o ambigui y, and
allows a clea in e p e a ion o he ela ionships ex ac ed. I does no use lea ning
p ocess. Unlike p e ious wo k [5], he p oposed algo i hm does no need an ini ial lis
o e-mails and i uses heu is ics o d i ing he cons uc ion o a social ne wo k by
opic and by impo ance o he ne wo k membe s.
The p ac ice cases p esen ed in his pape ha e been de eloped wi h TREDAR
ool. This ool pe mi s o de ine business p ocesses in an in e ac i e way. The speci ic
analyzed case is a compe i i e in elligence p ocess [19] ha pe mi s ecollec and
ex ac da a om in e ne abou VSST communi y and pe o m di e en ypes o
analyses o he decision making o sol e di e en que ies. This ool also allows us o
sp ead and isualize he ob ained da a o decision-making suppo . In his way, all he
necessa y s ages o p o ide a igilance se ice and compe i i e in elligence a e
in eg a ed h ough web echnology.
3 Social ne wo ks opic-d i en ex ac ion algo i hm
In his sec ion he algo i hm o social ne wo ks opic-d i en ex ac ions o malize
in [6] is desc ibed. The algo i hm is di ided in o h ee s eps:
• To seed e-mails add ess ex ac ion: To s a he ex ac ion p ocess is
necessa y o ha e a se o e-mails. Di e en p ocesses o ex ac ing e-mail
add esses om web can be de ined. The e-mails add ess selec ion p ocess
used by de algo i hm is desc ibed below:
o To pe o m que ies wi h he ‘< opic>‘ and isi he web documen s
e u ned by he sea ch engine
o To ex ac he e-mails om he <a> ags o he h ml pages and
o Fo each e-mail add ess, o check h ough he que y ‘<email>’
‘< opic>’ he associa ion deg ee o e-mail o he opic. The
p ocess conside s a high associa ion deg ee i he numbe o
documen s e u ned by he p e ious que y exceeds a h eshold.
• To expand he e-mail add ess: The algo i hm expands he social ne wo k
wi h new ela ionships ex ac ed om sea ch engine que ies esul . Fo
each mail add ess he algo i hm do:
o To c ea e a que y using he schema: ‘<use name>’ ‘<domain>’
ile ype:pd . Fo example, he use name o he e-mail add ess
osa @ us.es is osa and he domain is us.es, hence he que y
is: ‘ osa ’ ‘us.es’ ile ype:pd and ex ac s he esul con ex s.
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o The con ex s we e analyzed using egula exp essions, o analyze
only he con ex s in which e i y he e-mail appea ance. Each new
e-mail ha appea s in he con ex is added o he social ne wo k as
a new node and he ela ionships a e added. The nodes and he
ela ionships a e associa ed wi h a coun e which will ep esen
hei impo ance in he ne wo k. I any e-mail add ess appea s
again a some con ex , he coun e will inc ease by one uni , also
he ela ionships.
• Social ne wo ks opic-d i en: This p incipal p ocess is i e a i e and s o es
he e-mails add ess in a p io i y queue.
o Ini ially, he queue is ini ialized wi h he e-mails add ess seeds.
o In each i e a ion an e-mails add ess is ex ac ed om he queue op,
and i is checked he associa ion deg ee o he e-mail add ess o
he opic. I he associa ion deg ee exceeds a h eshold, hen i is
expanded wi h he neighbo s o he e-mails add ess.
o The i e a ion is epea ed un il he queue is emp y and o he
s a egies can be implemen ed, o example limi ing he numbe o
web pages isi ed o he numbe o emails-add ess o he social
ne wo k.
o A e comple ing he expansion, he new nodes a e added o he
queue.
Al hough i is possible ha a pe son can ha e mo e han one e-mail add ess ( o
example a pe sonal and a ins i u ional e-mail add ess), i is also ue ha an e-mail
add ess ypically iden i y a pe son. I could a oid he ambigui y p oblems o o he
me hodologies, bu no he a ie y p oblems. Rea anging he queue se e al d i en
s a egies can be implemen ed: maximizing he associa ion deg ee o e-mail o he
opic o o he social ne wo ks analysis measu es such as he deg ee, page ank o he
be weenness.
4 Compe i i e In elligence Tasks
In o de o illus a e he social ne wo ks opic-d i en ex ac ion echnique, he
‘VSST’ and ‘In e lligence compe i i e’ opic has been used. The goal is he ex ac ion
o he social ne wo k o he communi y o he se e al Veille S a égique Scien i ique
& Technologique (VSST) edi ions. The 327 seeds we e ex ac ed om di e en
VSST web pages edi ions. A e unning he ex ac ion algo i hm, he social ne wo k
had 2.107 nodes and 7.102 edges. O hese nodes, 432 nodes exceeded he minimum
h eshold, only hese nodes we e expanded.
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Table 1. Numbe s o documen s and que ies associa ed o each opic
The social ne wo k ex ac ed can be used o ind expe s in ce ain opics, [15] uses
he numbe o pages o de elop expe s anking. In ou case we can use he que y
<email> "< opic>" o es ima e de numbe o documen s associa ed and de elop a
opic anking o expe s, as i is shown in Table 1 and Table 2. The e a e al e na i es
o he impac measu e such as Mindsha e. The ad an age ha we ha e p oposed in he
expe s inding ask is he own o he social ne wo k, his allows using he ARS
measu es o classi y he expe s, o example: deg ee, au ho i y, cen ali y, be weens,
e c [16].
Table 2. Numbe s o documen s associa ed o each opic
An app op ia e selec ion o email add esses can be used o model an a ea o
in e es and op imize he ga he e o in o ma ion. Fo example, using he que y
<email> ile ype:pd o download pd documen s o expe s in he a ea. This
ga he ing o in o ma ion can be used o e ine he expe anking. In o de o analyze
he global opics impac o he social ne wo k, i is possible analyzing he impac o
p oduc s. In [5] his app oach is used o analyze wo ARS ools, Pajek and Ucine . I
concluded ha he global impac o Ucine on he social ne wo k was a 20% la ge
han Pajek. P obably he di e ence o impac is due o i s usabili y. The mayo
p oblem o his app oach is he wo ds ambigui y.
By using Figu e 1 i is possible o analyze he coope a ion ela ions be ween he
coun ies o he VSST pa icipan s. In his case unde lines he s a egic posi ion o
F ance and his his o ical connec ing wi h he Magh eb coun ies. And using Figu e 2
is possible o analyze he coope a ion be ween di e en o ganiza ions h ough hei
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Fig. 1. VSST coun ies collabo a ions
domains. Fo example, analyze he domains we can disco e new compe i o s (IALE,
CDE, ISCOPE, IMCSLINE, e c), new clien s (LAPOSTE, CEA, EADS, e c) o new
in es iga ion cen e s (IRIT, INIST, e c). And analyzing he ela ionships we can
app ecia e he cen al posi ion o di e se uni e si ies in he ne wo k. Also using he IP
domains is also possible o alloca e he o ganiza ions [5].
Figu e 3 ep esen s in a lexical ne wo k he VSST communi y opics o in e es . In
o de o build his map, wo asks ha e been execu ed: (1) he key wo ds o he pape
published in VSST e en s in 2007 ha e been ex ac ed (2) he concu ency ne wo k
has been calcula ed [18]. Using his map, i is possible o de e mine he in e es ing
cen e s o he esea che ha pa icipa e in he e en s. The ollow opics o in e es
ha e been ex ac ed in his example: ex -mining, na u al language p ocessing,
c ea i i y and inno a ion, in o ma ion e ie al and il e ing, co-wo d analysis,
business-in elligence.
The esul s o he VSST case s udy a e online a ailable in
h p://www.lsi.us.es/~ osa /index.php/F osa /MapaLis aVSST2007Es.
5 Conclusions and u u e wo k
In his pape some echniques ha e been combined helping in he making-decision
p ocess o an esea ch o ganiza ion. The opics ha ha e been used a e : ex ac ion o
in o ma ion om he web, analysis o social ne wo ks and co-occu ences.
The que ies ha can be sol ed wi h hese echniques a e :
• Which a e he expe s in an speci ic a ea?
• Which a e he mos in luence g oups ?
• How he o ganiza ions a e s uc u ed?
• Which a e he mos in luen ial coun ies ?
• Which a e he goal ne wo k?
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This wo k p o ides a amewo k o making-decision p ocesses o analyse he
dis ibu ed da a in di e en and he e ogeneous sou ces, non cen e ed in any da abase.
Figu e 4 shows in a isual way he main ideas de eloped in his wo k.
As u u e wo k we p opose an au oma ic classi ica ion o he domains using he
a ailable in o ma ion in he esea h cen e webs. Also i will be in e es ing o de elop
echniques o ex ac in o ma ion abou he en i ies o be ep esen ed in he maps
(names, depa men s, elephone numbe s…)
Acknowledgmen s. This wo k has been pa ially unded by Jun a de Andalucía
(P08-TIC-04095).
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Fig. 2. VSST o ganiza ions collabo a ions
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