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Methodologie du classement international des institutions d enseignement superieur et de recherche elabore par l'Universite de Leiden (Pays Bas)

Methodologie du classement international des institutions d enseignement superieur et de recherche elabore par l'Universite de Leiden (Pays Bas)

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    Classement Leiden Ranking Classement Leiden Ranking Presentation Transcript

    • New developments in bibliometric methods: evaluation, mapping, ranking Ton van Raan Atelier Bibliométrie de l’URFIST de Paris 23 mars 2012 – CNAM Center for Science and Technology Studies (CWTS) Leiden University
    • Leiden University oldest in the Netherlands, 1575European League of Research Universities (LERU) Within world top-100 12 Nobel PrizesLeiden, historic city (2th, 11th C.), strong cultural (arts, painting) & scientific tradition one of the largest science parks in EU 2
    • Contents of this presentation:* Bibliometric methodology: • impact • maps* Leiden Ranking 2011-2012: new indicators* Evaluation tools related to the Leiden Ranking
    • Total publ universe non-WoS publ: Books Book chapters Conf. proc. ReportsWoS/Scopus sub-universejournal articles only,> 1,000,000p/y Non-WoS journals VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002 VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS Behavior in “Scale-Free” Network Models Truncation of Power Law 1 April 2002 due to Information Filtering Truncation of Power Law Behavior in “Scale-Free” Network ModelsVOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002 due to Information Filtering Barthélémy,3 H. Eugene Stanley,1 and Luís A. Nunes Amaral1 Stefano Mossa,1,2 Marc VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 Center forApril 2002 and Department of Physics, Boston University, Boston, Massachusetts 02215 1 Polymer Studies Truncation of Power Law Behavior in “Scale-Free” Network Models Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and LuísUdR, and INFM Center for Statistical Mechanics and Complexity, Information Filtering 2 Dipartimento di Fisica, INFM A. Nunes Amaral1 due to Truncation of Power Law1 Behavior in “Scale-Free” Network Models di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy Università Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215 2 Dipartimento di Fisica, INFM UdR, and3INFM Center for Statisticalde la Matière Condensée, BP 12, 91680 Bruyères-le-Châtel, France due to Information Filtering CEA-Service de Physique Mechanics and Complexity, Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and Luís A. Nunes Amaral1 (Received 18 October 2001; published 14 March 2002) Università di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy 1 Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215 Stefano Mossa,1,2 Marc Barthélémy,3CEA-Service de Physique Luís A.We formulate a general 91680 Bruyères-le-Châtel, of scale-free Dipartimento di Fisica, INFM UdR, and INFM Center for Statistical Mechanics and Complexity, 3 H. Eugene Stanley,1 and de la Matière Condensée, BP 12, Nunes Amaral1 France 2 (Received Boston, Massachusetts 02215 model 2002)the growth 18 October 2001; published 14 March for networks under filtering information 1 Center for Polymer Studies and Department of Physics, Boston University, conditions—that is, when the nodes can process information about only a subset of the di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy Università existing nodes in the 2 Dipartimento di Fisica, INFM UdR, and INFM Center for Statistical Mechanics and Complexity, distribution of the number of incoming linksCEA-Service de Physique de la Matière Condensée, BP 12, 91680 Bruyères-le-Châtel, France 3 We formulate a general model for the growth that the networks under network. We find to a node follows a universal scaling Università di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma,that itof scale-freepower law with an filtering information controlled not only(Received 18 October 2001; published 14 March 2002) Italy decays as form, i.e.,information about a exponential truncation by the system size 3 CEA-Service de Physique de conditions—that is, when the 91680 canbut also by a feature not previouslysubset of thethe subsetnodes in the “accessible” to the node. We test our la Matière Condensée, BP 12, nodes Bruyères-le-Châtel, France only a considered, existing of the network process (Received network. We find published 14 March 2002) number of incoming for theto a node follows aand find agreement. 18 October 2001; that the distribution of the with empirical data links World Wide Web universal scaling We formulate a general model for the growth of scale-free networks under filtering information form, i.e., that it decays as a power law modelan exponential truncation controlled not only by the system size with conditions—that is, when the nodes can process information about only a subset of the existing nodes in the We formulate a general model for the growth of scale-free networks under the subset of the network “accessible” to the node. We test ourWe find that the distribution of the number of incoming links to a node follows a universal scaling but also by a feature not previously considered, filtering information network. DOI: 10.1103/PhysRevLett.88.138701 PACS numbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc conditions—that is, when the nodes canmodel with empirical data for the World Widethe existing nodes in the process information about only a subset of Web and find agreement. form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size network. We find that the distribution of the number of incoming links to a node follows a universal scaling but also by a feature not previously considered, the subset of the network “accessible” to the node. We test our form, i.e., that it decays as a power law with an exponential truncation controlled not onlynumbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc model with empirical data for the World Wide Web and find agreement. DOI: 10.1103/PhysRevLett.88.138701 PACS by the system size but also by a feature not previously considered, the subset of theThere is a “accessible” to the node. We in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide network great deal of current interest test our model with empirical data for the World Wide Web and find agreement. DOI: 10.1103/PhysRevLett.88.138701 PACS numbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc Web (WWW) [4,5] and the Internet [6]. Network structure is critical in many contexts such as Internet attacks [2], spread of an Email virus [7], or There is a great deal of current interest in understanding the epidemics [8].growththese problems, global networks [1–3], such as theof links Widean important role on the dynamics of the dynamics of human structure and In all mechanisms of the nodes with the largest number World play DOI: 10.1103/PhysRevLett.88.138701 PACS numbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc the global structure of the network as well as its precise distribution of the number of links. Web (WWW) [4,5] and the Internet [6]. NetworkItstructure is critical in many contexts such as Internet attacks [2], spread of an Email virus [7], or system. is therefore important to know dynamics of human epidemics [8]. In all Recent empiricalthe nodesreportthe largest the Internetlinks playWWW have scale-free properties; that understanding the structure and growththe these problems, studies with that both number of and the an important deal on current interestofinthe the number of incoming links and mechanisms of global networks [1–3], such as the World Wide There is a great role of the dynamics is, system. It is therefore important to know number of structure of the network as node as its precise distribution of the number ofthe Internet [6]. Network structure is critical in the scale-free such as Internet attacks [2], spread of an Email virus [7], or the global outgoing links at a given well have distributionsWeb (WWW) [4,5] and law tails [4–6]. It has been proposed [9] that many contexts that decay with power links. Recent empirical studies report growthstructure of theand the networksWWW such be the World bydynamics of human incoming [8]. all the the Internet Internet and the [1–3], may explained Widemechanism referred to as “preferential attachment” [10] in which new nodes link a There is a great deal of current interest in understanding the structure andthat bothmechanisms of globalWWW have scale-freeasproperties; that is, the number of epidemics linksInand these problems, the nodes with the largest number of links play an important role on the dynamics of the number structure is links at a given node have distributions thatprobability proportional to the numberhas existing links toimportant to know the focus on the stochastic character well as to existing nodes with a decay with power [4–6]. system. It is proposed these nodes. Here we of been that structure of the network as of the Web (WWW) [4,5] and the Internet [6]. Networkof outgoing critical in many contexts such asattachment mechanism, which anlaw tailsvirus [7],Itorfollowingtherefore [9]nodesthe scale-free globalthe existing nodes with the largest its precise distribution of the number of links. preferential Internet attacks [2], spread of weEmail understand in theattachment” way: in which new nodesconnect to [10] New want to dynamics of human epidemics [8]. In allstructure of the Internet and the the largest number of linksby awithimportant role onto asdynamics ofRecent empirical studies report that to alink Internet and the For a large network it properties; that is, the number of incoming links and the these problems, the nodes with WWW may be explained playmechanism referred the “preferentialthe an number of links—i.e., of the largest degree—because of thewe focus offered by being linked of the advantages both the well-connected node. WWW have scale-free system. It is therefore important to knowto existing structure of a probabilityas well as its precise numbernewexistingnumber of links. of all number ofon the stochastic charactermake a decision on which node to connect with law tails [4–6]. It has been proposed [9] that the scale-free the global nodes with the network proportional to the distributionnode will know these nodes. Here existing nodes, so a new node given node have distributions that decay with power plausible that a of the links to the degrees outgoing links at a is not understand in the following way: New nodes want to connect to the existing nodes with the largest must Recent empirical studies report that both the Internet and the WWW have which we properties; that is, theit number of the state of the and structure of the Internet and the WWW may be explained by a play as nodes with ato as “preferential attachment” [10] in which new nodes link preferential attachment mechanism, scale-free based on what information has about incoming links network. the mechanism referred number distributions with the with degree—because of the has been offered [9] that the scale-free The preferential attachment mechanism then comes into number of outgoing links at a given node have of links—i.e.,that decaylargest power law tails [4–6]. Itadvantagesproposedby being linked to ato existing nodesnode. For a large network it to the number of existing links to these nodes. Here we focus on the stochastic character of the well-connected with a probability proportional is not plausible that mechanism referred to degreesare moreattachment” [10] known. new nodes link larger degree of all likely to become structure of the Internet and the WWW may be explained byaanew node will know the as “preferentialexisting nodes, so ainnew node must make a decisionattachmentnode to connect with understand in the following way: New nodes want to connect to the existing nodes with the largest which preferential on which mechanism, which we Refs > non-WoS to existing nodes with a probability proportional to the informationexistingabout to these nodes. Here we focus preferential attachment mechanism then links—i.e., with the nodes with a based on what number of it has links the state of the network. The on the stochastic character of number of comes into play as largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it the preferential attachment mechanism, which we understand more likely to become New nodes want to connect to the existing nodes with the largest plausible that a new node will know the degrees of all existing nodes, so a new node must make a decision on which node to connect with larger degree are in the following way: known. is not number of links—i.e., with the largest degree—because of the advantages offered by being linked to a well-connected node. For a large network it on what information it has about the state of the network. The preferential attachment mechanism then comes into play as nodes with a based is not plausible that a new node will know the degrees of all existing nodes, so a new node must make a decision on which node to connect with degree are more likely to become known. larger based on what information it has about the state of the network. The preferential attachment mechanism then comes into play as nodes with a larger degree are more likely to become known. 4
    • Aliens from GalaxyM35-245 approach planetEarth. They have accessto the Web of Science…..
    • pa1 pa2 pa3 pa4 citing p <> cited p pb1 pb2 pb3 pb4 pb5 Primary Citation Network Co-citation-network Bibliogr.coupl.network pb2 pa2 cited p <> cited p citing p <> citing ppb1 pb3 pa1 pa4 pb5 pa3 pb4
    • Primary network is a structure of different items(e.g., citing publ <> cited publ; citing publ <> concepts)In-degree primary network => received citations >impactSecondary network is a structure of similar items(e.g., citing publ<> citing publ; concepts <> concepts)Similarity in the secondary networks => co-occurrencemap
    • 1. Bibliometric methodology: impact
    • Citing Publications VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002 Truncation of Power Law Behavior in “Scale-Free”Network Models due to Information Filtering S te fa no Mossa,1,2 Marc Barthélém y,3 H. Eugene Stanle y,1 and Luís A. Nunes Am aral1 VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 200 2 1 Cente r for P oly mer Studie s and Department of Phys ic s, Bosto n University , B osto n, Massachusett s 02215 2 Dip artim ento di Fisica, IN F M UdR , and INFM Center for Sta tistic al Mechanic s and Complexit y, Truncation of Power Law Behavior in “Scale-Free” Network Models Università di R oma “La Sapienza,” Piazzale A ld o M oro 2, I-00185, Roma, Ita ly 3 CE A-Ser vice de Phys iq ue de la Matière Condensée, B P 12, 91680 Br uyèr es-le-Châtel, France due to Information Filtering (Received 18 Octo ber 2001; publi shed 14 March 2002) Ste fa no M ossa ,1 ,2 M arc B arth élémy ,3 H . Eu g en e Stan ley ,1 a nd Lu ís A. N un es Ama ral1 We formulate a general model fo r th e grow th of scale -fre e networks under filt ering informati on 1 C en te r fo r Po lym er S tu d ies an d D ep artme nt o f P hy sics, Bo sto n U niversity , Bo ston , M assa ch u setts 0 22 1 5 2 Dipa rtim en to d i Fisica, INF M U d R, a n d IN FM Cen te r for Statistica l Me cha n ics an d Co m plex ity , condit io ns— th at is , when th e nodes can process informati on about only a subset of th e existing nodes in the networks leading, possibly, to different dynamics, e.g., for the initiation and spread of epidemics. U n ive rsità di Ro m a “La S a pienz a,” Pia zza le Ald o Mo ro 2, I-00 18 5 , Ro m a, Ita ly netw ork. W e fin d th at the distrib utio n of the num ber of in coming li nks to a node foll ows a univ ersal s cali ng 3 C EA-S ervice de Ph ysiqu e d e la Ma tière Co n de nsé e, BP 1 2, 9 16 8 0 B ruy ères-le -Ch âtel, Fra n ce form , i. e., th at it decays as a power law with an exponenti al tr uncati on controlle d not only by th e s ystem siz e (Re ceive d 18 O ctob er 2 00 1 ; p u blish ed 1 4 Ma rc h 20 02 ) but als o by a featu re not previo usly considered, the subs et of the network “accessible ” to th e node. We test our model with empirical data for th e World W id e Web and fi nd agreement. W e fo rmulate a g en eral mod el fo r th e g row th of sc ale-free n etwo rks un de r filtering info rma tion co n dition s— that is, w h en th e no de s can pro cess info rm ation ab ou t o n ly a sub set of th e ex istin g no de s in th e DOI: 10.1103/P hysRevL ett.88.138701 PACS numbers : 89.2 0.Hh, 84.35.+i, 89.7 5.Da, 89.7 5.Hc n etw o rk. W e find th at the d is trib utio n o f th e nu mb er of in co min g lin ks to a n od e fo llow s a u niversa l sca ling In the context of network growth, the impossibility of knowing the degrees of all the nodes comprising the network due to the filtering process— form, i.e. , th at it de cay s a s a po we r law with a n ex p on en tial tru n catio n c on tro lle d n ot on ly b y the s ystem siz e b ut a lso b y a fe atu re no t pre viou sly c on sid ered , th e sub set o f the ne tw o rk “ acc essible” to the n od e. W e test ou r mo de l w ith emp irical d ata fo r th e W o rld W id e W eb an d fin d agre emen t. T here is a great deal of current in terest in understanding th e structure and growth m echanism s of glo bal networks [1–3], such as the World Wid e W eb (WW W) [4,5 ] and th e Inte rnet [6]. Netw ork structu re is criti cal in many conte xts such as Inte rnet att acks [2], spre ad of an E mail vir us [7], or D O I: 10 .1 1 03 /Ph ysR ev Lett. 88 .1 38 7 01 PA CS n umb ers: 8 9. 20 .H h, 8 4.3 5 .+i, 8 9. 75 .D a, 89 .7 5 .H c dynam ics of human epidemics [8]. In all th es e proble ms, the nodes wit h the largest num ber of li nks pla y an im portant role on the dynamic s of th e s ystem . It is th erefore important to know th e glo bal s tr ucture of the netw ork as well as its precise dis tr ibutio n of the number of lin ks. Recent em pir ic al studies report th at both the Internet and the WW W have scale -f ree properti es; that is, th e number of incomin g lin ks and th e and, hence, the inability to make the optimal, rational, choice—is not altogether unlike the “bounded rationality” concept of Simon [17]. number of outgoin g lin ks at a given node have distrib uti ons th at decay wit h power law tails [4 –6]. It has been proposed [9] th at the scale-free Th ere is a grea t d ea l of curren t in te re st in u nd erstan ding the stru ctu re an d g row th me ch an isms of g lob al n etw o rks [1 – 3], su ch as th e W orld W id e W eb (W W W ) [4, 5] a nd the Interne t [6 ]. N etwo rk struc tu re is critical in ma ny c on te xts su ch as In te rnet a ttack s [2 ], sp read o f an Ema il v irus [7], or s tr ucture of th e Inte rnet and the W WW may be explained by a mechanis m referred to as “preferential att achment” [10] in whic h new nodes lin k d yn amics of h um an ep id emics [8 ]. In all th ese pro blems, th e no de s w ith th e larg est nu mb er o f link s play an imp orta nt ro le on the d yn amics of th e to existin g nodes with a probabili ty proporti onal to the num ber of existi ng lin ks to th ese nodes. Here we focus on th e stochasti c characte r of th e sy ste m. It is th erefore imp ortan t to kn o w th e g lo b al stru ctu re o f the ne tw o rk as w ell as its p re cis e d istrib utio n of the n umb er o f lin ks. prefe rentia l atta chment mechanism, whic h we understand in the follo wing way: New nodes w ant to connect to th e existin g nodes wit h the la rgest R ece nt emp iric al stud ie s re po rt th at bo th th e Intern et a nd th e W W W ha ve sc ale -free p rop erties; th at is, the n u mbe r o f inc om in g lin ks an d th e number of li nks —i.e ., wit h the largest degree— because of the advanta ges off ered by bein g lin ked to a well -c onnected node. For a large network it n umb er of ou tgo in g lin ks at a give n n od e h av e distrib u tion s th at d eca y w ith p ow er law tails [4– 6]. It h as b een pro po sed [9 ] th at the sc ale-free is not plausible that a new node w il l know th e degrees of all existin g nodes, so a new node m us t make a decision on whic h node to connect wit h VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002 stru cture o f th e Interne t and th e WW W may b e ex p laine d by a me ch an ism referre d to a s “ prefere ntial a ttach men t” [1 0] in w h ic h ne w n od es link based on what informati on it has about the sta te of th e netw ork. The prefe rentia l att achment mechanis m then comes in to play as nodes with a Remarkably, it appears that, for the description of WWW growth, the preferential attachment mechanism, originally proposed by Simon [10], to ex istin g no d es with a p rob ab ility pro po rtio n al to th e nu mb er o f ex isting lin ks to th ese n od es. H ere w e fo cu s o n the stoc ha stic ch aracter of the larger degree are m ore lik ely to become known. p re fe ren tia l attac hme nt me ch an ism, w hich w e u n derstan d in the follo w in g w ay : N ew no de s w an t to c on ne ct to th e e xisting no de s w ith the la rg est Truncation of Power Law Behavior in “Scale-Free”Network Models n umb er o f link s— i.e ., w ith the larg est d egre e— b ec au se o f th e ad van ta ge s o ffered b y b ein g link ed to a w ell-co n ne cte d no de . Fo r a larg e netw ork it due to Information Filtering is n ot p la usible th at a ne w n od e w ill k no w the d eg re es of a ll ex is tin g n od es, so a n ew n od e mu st mak e a d ec ision o n w h ich n o de to co nn ec t w ith b ased on wh at in fo rmatio n it ha s abo u t th e state of th e n etw o rk. Th e p re feren tial atta ch men t mec ha nism th en c ome s in to p lay a s n o de s w ith a large r deg ree are more like ly to b ec ome k no w n. S te fa no Mossa,1,2 Marc Barthélém y,3 H. Eugene Stanle y,1 and Luís A. Nunes Am aral1 must be modified along the lines of another concept also introduced by him—bounded rationality [17]. 1 Cente r fo r Poly mer Studie s and Department of Phys ic s, Bosto n University , B osto n, Massachusett s 02215 VOLUM E 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002 2 Dip artim ento di Fisica, IN F M UdR , and INFM Center for Sta tistic al Mechanic s and Complexit y, Università di R oma “La Sapienza,” Piazzale A ld o M oro 2, I-00185, Roma, Ita ly 3 CE A-Ser vice de Phys iq ue de la Matière Condensée, B P 12, 91680 Br uyèr es-le-Châtel, France Truncation of Power Law Behavior in “Scale-Free”Network Models (Received 18 Octo ber 2001; publi shed 14 March 2002) due to Information Filtering We formulate a general model fo r th e grow th of scale -fre e networks under filt ering informati on Stefano Mossa,1 ,2 Marc Barth élém y,3 H. Eugene Sta nle y,1 and L uís A. Nunes Amaral1 condit io ns— th at is , when th e nodes can process informati on about only a subset of th e existing nodes in the 1 Center for Polymer Stu dies and Department of Phys ic s, B osto n Univer sit y, Boston, M assachusetts 02215 netw ork. W e fin d th at the distrib utio n of the num ber of in coming li nks to a node foll ows a univ ersal s cali ng 2 Dipar ti mento di F isica, INF M UdR, and INFM Center fo r Stati stic al M echanics and Comple xity , form , i. e., th at it decays as a power law with an exponenti al tr uncati on controlle d not only by th e s ystem siz e Univ ers it à diR oma “La Sapienza,” Piazzale Aldo M or o 2, I-00185, Roma, Italy but als o by a featu re not previously considered, the subs et of the network “accessible ” to th e node. We test our 3 CEA -Service de Physiq ue de la M atière Condensée, B P 12, 91680 Bruyères-le -C hâte l, Fr ance model with empirical data for th e World W id e Web and fi nd agreement. (Received 18 Octo ber 2001; publi shed 14 March 2002) DOI: 10.1103/P hysRevL ett.88.138701 PACS numbers : 89.2 0.Hh, 84.35.+i, 89.7 5.Da, 89.7 5.Hc We formula te a general m odel for th e grow th of scale -fr ee networks under fil te rin g informati on We thank R. Albert, P. Ball, A.-L. Barabási, M. Buchanan, J. Camacho, and R. Guimerà for stimulating discussions and helpful suggestions. We conditi ons —that is, when the nodes can process informati on about only a subset of the existin g nodes in th e netw ork. We fi nd that th e distr ibutio n of the number of in coming lin ks to a node fo llo ws a universal scalin g fo rm, i.e ., that it decays as a power law with an exponenti al tr uncation contr olle d not only by the syste m size T here is a great deal of current in terest in understanding th e structure and growth m echanism s of glo bal networks [1–3], such as the World Wid e but also by a featu re not previous ly cons id ered, the subs et of the netw ork “accessib le” to the node. W e te st our W eb (WW W) [4,5 ] and th e Inte rnet [6]. Netw ork structu re is criti cal in many conte xts such as Inte rnet att acks [2], spre ad of an E mail vir us [7], or model wit h em pir ical data for the World Wid e Web and fi nd agreement. dynam ic s of human epidemics [8]. In all th es e proble ms, the nodes wit h the largest num ber of li nks pla y an important role on the dynamics of th e s ystem. It is th erefore important to know th e glo bal s tr ucture of the netw ork as well as its precise dis tr ibutio n of the number of lin ks. DOI: 10.1 103/PhysRevLett .8 8.1 38701 PACS numbers: 89.2 0.Hh, 84.3 5.+ i, 89.75.Da, 89.75.Hc are especially grateful to R. Kumar for sharing his data. We thank NIH/NCRR (P41 RR13622) and NSF for support. Recent em pir ic al studies report th at both the Internet and the WW W have scale -fr ee properti es; that is, th e number of incomin g lin ks and th e number of outgoin g lin ks at a given node have distrib uti ons th at decay wit h power law tails [4 –6]. It has been proposed [9] th at the scale-free s tr ucture of th e Inte rnet and the W WW may be explained by a mechanism referred to as “preferential att achment” [10] in whic h new nodes lin k to existin g nodes with a probabili ty proporti onal to the num ber of existi ng lin ks to th ese nodes. Here we focus on th e stochasti c characte r of th e T here is a great deal of current interest in unders ta nding the structure and growth mechanisms of global networks [1 –3], such as the World Wid e prefe rentia l atta chment mechanism, whic h we understand in the follo wing way: New nodes w ant to connect to th e existin g nodes wit h the la rgest W eb (WW W) [4,5 ] and the In ternet [6]. Netw ork structu re is cri tical in many contexts s uch as Inte rn et atta cks [2], spread of an Email viru s [7], or number of li nks —i.e ., wit h the largest degree— because of the advanta ges off ered by bein g lin ked to a well -c onnected node. For a large network it dynamics of human epid emics [8 ]. In all these problems, the nodes wit h the larges t num ber of lin ks play an im porta nt role on th e dynamics of th e is not plausible that a new node w il l know th e degrees of all existin g nodes, s o a new node m us t make a decision on whic h node to connect wit h s ystem . It is therefo re im portant to know the global str ucture of the netw ork as well as its pre cise distrib uti on of th e number of lin ks. based on what informati on it has about the sta te of th e netw ork. The prefe rentia l att achment mechanis m then comes in to play as nodes with a Recent empiric al stu dies report that both the Inte rnet and the WW W have scale-free properties; th at is, the num ber of incoming lin ks and the larger degree are m ore lik ely to become known. number of outgoing lin ks at a giv en node have dis tr ib utio ns th at decay wit h power law tails [4–6]. It has been propos ed [9] that the scale-free s tr ucture of th e Inte rnet and th e WWW m ay be explained by a mechanism referred to as “prefe rentia l atta chm ent” [1 0] in which new nodes lin k to existin g nodes with a probabili ty proportio nal to th e number of existi ng lin ks to th es e nodes . Here we focus on the sto chastic character of th e prefe renti al att achment m echanism , which we understand in the fo llo wing way: New nodes want to connect to the existing nodes with th e la rgest number of li nks— i. e., wit h th e la rgest degree—because of th e advanta ges off ered by being li nked to a well-connecte d node. F or a large network it is not plausib le th at a new node will know the degrees of all existin g nodes, so a new node must m ake a decis io n on which node to connect wit h based on what in formatio n it has about the state of th e netw ork. The prefe rentia l att achm ent m echanism th en com es into pla y as nodes w it h a larger degree are more li kely to become known. Weight? [1] S. H. Strogatz, Nature (London) 410, 268 (2001). [2] R. Albert and A.-L. Barabási, Rev. Mod. Phys. 74, 47 (2002). [3] S. N. Dorogovtsev and J. F. F. Mendes, Adv. Phys. (to be published). [4] R. Albert, H. Jeong, and A.-L. Barabási, Nature (London) 401, 130 (1999). [5] B. A. Huberman and L. A. Adamic, Nature (London) 401, 131 (1999); R. Kumar et al., in Proceedings of the 25th International Conference on Very Large Databases (Morgan Kaufmann Publishers, San Francisco, 1999), p. 639; A. Broder et al., Comput. Netw. 33, 309 (2000); P. L. VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002 Truncation of Power Law Behavior in “Scale-Free” Network Models due to Information Filtering Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and Luís A. NunesAmaral1 1 Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215 2 Dipartimento di Fisica, INFM UdR, and INFM Center for Statistical Mechanics and Complexity, Universitàdi Roma “La Sapienza,”Piazzale Aldo Moro 2, I-00185, Roma, Italy Krapivsky, S. Redner, and F. Leyvraz, Phys. Rev. Lett. 85, 4629 (2000); S. N. Dorogovtsev, J. F. F. Mendes, and A. N. Samukhin, ibid. 85, 4633 3 CEA-Service de Physique de la Matière Condensée, BP 12, 91680 Bruyères-le-Châtel, France (Received 18 October 2001; published 14 March 2002) We formulate a general model for the growth of scale-free networks under filtering information (2000); A. Vazquez, Europhys. Lett. 54, 430 (2001). [6] M. Faloutsos, P. Faloutsos, and C. Faloutsos, Comput. Commun. Rev. 29, 251 (1999); G. Caldarelli, R. Marchetti, and L. Pietronero, conditions—that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling From other disciplines form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network “accessible” to the node. We test our model with empirical data for the World Wide Web and find agreement. DOI: 10.1103/PhysRevLett.88.138701 PACS numbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc Europhys. Lett. 52, 386 (2000); A. Medina, I. Matta, and J. Byers, Comput. Commun. Rev. 30, 18 (2000); R. Pastor-Satorras, A. Vazquez, and A. Vespignani, arXiv:cond-mat/0105161; L. A. Adamic et al., Phys. Rev. E 64, 046135 (2001).There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World WideWeb (WWW) [4,5] and the Internet [6]. Network structure is critical in many contexts such as Internet attacks [2], spread of an Email virus [7], ordynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on thedynamics of thesystem. It is therefore important to know the global structure of the network as well as its precise distribution of the number of links.Recent empirical studies report that both the Internet and the WWW have scale-free properties; that is, the number of incoming links and thenumber of outgoing links at a given node have distributions that decay with power law tails [4–6]. It has been proposed [9] that the scale-freestructure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes linkto existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of thepreferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes with the largest [7] F. B. Cohen, A Short Course on Computer Viruses (Wiley, New York, 1994); R. Pastor-Satorras and A. Vespignagni, Phys. Rev. Lett. 86, 3200 (2001); Phys. Rev. E 63, 066117 (2001).number of links—i.e., with the largest degree—because of the advantages offered by being linked to a well-connected node. For a large network itis not plausible that a new node will know the degrees of all existing nodes, so a new node must make a decision on which node to connect withbased on what information it has about the state of the network. The preferential attachment mechanism then comes into play as nodes with a From emerging fieldslarger degree are more likely to become known. [8] F. Liljeros, C. R. Edling, L. A. Nunes Amaral, H. E. Stanley, and Y. Åberg, Nature (London) 411, 907 (2001). [9] A.-L. Barabási and R. Albert, Science 286, 509 (1999). Cited Publications [10] Y. Ijiri and H. A. Simon, Skew Distributions and the Sizes of Business Firms (North-Holland, Amsterdam, 1977). [11] G. Bianconi and A.-L. Barabasi, Europhys. Lett. 54, 436 (2001). From research devoted to societal, [12] A. F. J. Van Raan, Scientometrics 47, 347 (2000). [13] We consider a modification to the network growth rule described earlier in the paper: at each time step t, the new node establishes m new links, where m is drawn from a power law distribution with exponent gout. economical and technological problems [14] For n(I) = const, one recovers the scale-free model of Ref. [9]. [15] It is known [11] that, for an exponential or fat-tailed distribution of fitness, the structure of the network becomes much more complex; in particular, the in-degree distribution is no longer a power law. Hence, we do not consider in this manuscript other shapes of the fitness distribution. From industry [16] L. A. N. Amaral, A. Scala, M. Barthélémy, and H. E. Stanley, Proc. Natl. Acad. Sci. U.S.A. 97, 11 149 (2000). [17] H. A. Simon, Models of Bounded Rationality: Empirically Grounded Economic Reason (MIT Press, Cambridge, 1997). From international top-groups These all f(t)!! > Sleeping Beauties
    • Intermezzo: statistical properties of the primary network 1 2 3 4 5 6 7 Out-degree (all)= 1 a b c d e f g In-degree (b) =5 Fig. 1: Citing and cited publications
    • Large differences among research fields in citationdensityand therefore:Citation counts must always be normalized to field-specific valuesH-index is not normalized and inconsistent
    • C 3000 100,000 450,0002007-2010P 150,0002007- 500 25,0002010 Dept. Radiology Radiology journals Radiology field CPP = 6 JCSm = 4 FCSm = 3 CPP/JCSm=1.5 CPP/FCSm = 2
    • • Two normalization mechanisms: ∑=1 ci n ∑ n n ci CPP/FCSm = i = i=1 ∑ ∑ n n i=1 ei n i=1 ei 1 n ci MNCS = n ∑ i = ei 1 ci: actual number of citations of publication i ei: expected number of citations of publication i• Value of 1 indicates performance at world average 13
    • Basic research in natural and medical science high FCSm High CPP high FCSm, butlow FCSm, but low JCSmhigh JCSm low CPP low FCSm Δ Citation density ~20 !Applied research, engineering,social sciences
    • PHYSICS35000 time lag & citation window30000250002000015000100005000 Publications from 1991,….1995 0 Cits91 Cits92 Cits93 Cits94 Cits95 Cits96 Cits97 Cits98 Cits99 Cits00 Cits01 Cits02
    • * SOCIOLOGY250020001500Again: never use journal impact factors!!1000500 0 Cits91 Cits92 Cits93 Cits94 Cits95 Cits96 Cits97 Cits98 Cits99 Cits00 Cits01 Cits02
    • Departments ‘bottom-up’ analysis: input data (assignment of researchers to departments) necessary; > Detailed research performance analysis of a university by departmentUniversity Fields ‘top-down’ analysis: field-structure is imposed to university; > Broad overview analysis of a university by field
    • Crown indicator CPP/ CPP/ JCSm/ P C+sc CPP Pnc JCSm FCSm JCSm FCSm FCSm SelfCitDept of Radiology, UMC1997 - 2009 H = 49 1,045 20,552 16.73 17% 13.63 9.84 1.23 1.70 1.36 15%1997 - 2000 196 616 2.32 51% 2.16 1.92 1.07 1.21 1.09 26%1998 - 2001 213 938 3.43 38% 3.30 2.60 1.04 1.32 1.23 22%1999 - 2002 243 1,151 3.63 39% 4.00 3.00 0.91 1.21 1.29 23%2000 - 2003 231 1,590 5.61 36% 4.78 3.14 1.17 1.79 1.47 18%2001 - 2004 221 1,576 5.94 29% 4.34 3.03 1.37 1.96 1.39 17%2002 - 2005 306 2,097 5.72 40% 3.94 2.79 1.45 2.05 1.37 17%2003 - 2006 358 2,516 5.88 35% 3.71 2.65 1.58 2.21 1.36 16%2004 - 2007 417 3,368 6.82 35% 4.37 3.03 1.56 2.25 1.40 16%2005 - 2008 492 4,254 7.10 30% 5.08 3.73 1.40 1.90 1.33 18%2006 - 2009 H497 27 = 3,986 6.42 30% 5.08 3.52 1.26 1.82 1.41 20%
    • Impact trend of two depts. Radiology (UMC A and B) 3.00 P(2006-9)=561: 2.50 in which fields? 2.00 above 1.50CPP/FCSm 1.00 below international 0.50 level 0.00 1997 - 1998 - 1999 - 2000 - 2001 - 2002 - 2003 - 2004 - 2005 - 2006 - 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 years
    • 2006 - 2009 FIELD Radiology UMC (CPP/FCSm) RAD,NUCL MED IM (1.63)CARD&CARDIOV SYS (2.24) SURGERY (1.71) CLIN NEUROLOGY (2.15) ONCOLOGY (1.55) Research Profile PERIPHL VASC DIS Output (P) and Impact (CPP/FCSm) (1.20) 2006 - 2009 PEDIATRICS (1.09)MEDICINE,GEN&INT Dept Radiology UMC (4.02) NEUROSCIENCES (2.71)GASTROENTEROLOGY (0.59)
    • 1992 - 2000 PUBLICATIONS AND IMPACT PER SUBFIELD Diseases of the Neurosystem - 2000 1992 Dept Output in Fields Erkrankungen des Nervensystems Dept Impact from Fields Erkrankungen des Nervensystems CPP/ SUBFIELD FCSm (CPP/FCSm) Neurosc NEUROSCIENCES 2.28 (2.28) Biochem & MOL BIOL 2.00 BIOCH (2.00) CPP/ SUBFIELD (CPP/FCSm) Cell Biol BIOLOGY 1.96 CELL (1.96) FCSm Devel Biol DEVELOPMENT BIOL 1.33 (1.33) Neurosc 2.70 NEUROSCIENCES (2.70) multidisc Knowledge use by very MULTIDISCIPL SC 2.84 (2.84) Genetics & HERED 2.70 GENETICS (2.70) Biochem 1.92BIOCH & MOL BIOL (1.92) Physiol 1.74 PHYSIOLOGY (1.74) high impact groups Cell Biol (2.74) CELL BIOLOGY 2.74 Clin Neurol CLIN NEUROLOGY 1.58 (1.58) Pharmacol & PHAR 3.94 PHARMACOL (3.94) multidisc SC (4.33) MULTIDISCIPL 4.33 OncologyONCOLOGY 1.60 (1.60) Biology 2.06 BIOLOGY (2.06) Devel Biol 1.32EVELOPMENT BIOL (1.32) Anat ANAT & MORPHOL 2.15 Morph (2.15) Endocrinol & METAB 1.81 ENDOCRIN (1.81) 0% 10% 20% 30% 40% 50% 60% Percentage of Publications Pathology PATHOLOGY 2.11 (2.11) Urology & NEPHRO 1.30 UROLOGY (1.30) Biophysics BIOPHYSICS 1.97 (1.97) Immunol IMMUNOLOGY 1.84 (1.84) IMPACT: LOW AVERAGE HIGH Res Medic MEDICINE, RES 1.88 (1.88) Zoology 4.51 ZOOLOGY (4.51) Opthalmol OPHTHALMOLOGY 2.21 (2.21) 0% 5% 10% 15% 20% 25% 30% 35% 40% Relative share of impact
    • 2. Bibliometric methodology: maps
    • Main field: Medical & Life Sciences Major field BIOMEDICAL SCIENCES ANATOMY & MORPHOLOGY IMMUNOLOGY INTEGRATIVE & COMPLEMENTARY MEDICINE MEDICAL LABORATORY TECHNOLOGY MEDICINE, RESEARCH & EXPERIMENTAL fields NEUROIMAGING NEUROSCIENCES PHARMACOLOGY & PHARMACY journals ACTA GENETICAE MEDICAE ET GEMELLOLOGIAE AMERICAN JOURNAL OF HUMAN GENETICS AMERICAN JOURNAL OF MEDICAL GENETICS PHYSIOLOGY ANIMAL BLOOD GROUPS AND BIOCHEMICAL GENETICS ANNALES DE GENETIQUE ANNALES DE GENETIQUE ET DE SELECTION ANIMALE ANNALS OF HUMAN GENETICS RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING ATTI ASSOCIAZIONE GENETICA ITALIANA BEHAVIOR GENETICS BIOCHEMICAL GENETICS CANADIAN JOURNAL OF GENETICS AND CYTOLOGY CANCER GENETICS AND CYTOGENETICS TOXICOLOGY CARYOLOGIA CHROMOSOMA CLINICAL GENETICS CURRENT GENETICS VIROLOGY CYTOGENETICS AND CELL GENETICS CYTOLOGIA DEVELOPMENTAL GENETICS ENVIRONMENTAL MUTAGENESIS EVOLUTION GENE GENETICA GENETICA POLONICA GENETICAL RESEARCH GENETICS GENETIKA HEREDITAS HEREDITY HUMAN BIOLOGY HUMAN GENETICS HUMAN HEREDITY IMMUNOGENETICSCitation relations between journals are INDIAN JOURNAL OF GENETICS AND PLANT BREEDING JAPANESE JOURNAL OF GENETICS JAPANESE JOURNAL OF HUMAN GENETICS JOURNAL DE GENETIQUE HUMAINE JOURNAL OF HEREDITY JOURNAL OF IMMUNOGENETICS JOURNAL OF MEDICAL GENETICSused to define fields of science JOURNAL OF MENTAL DEFICIENCY RESEARCH JOURNAL OF MOLECULAR EVOLUTION MOLECULAR & GENERAL GENETICS MUTATION RESEARCH PLASMID SILVAE GENETICA THEORETICAL AND APPLIED GENETICS THEORETICAL POPULATION BIOLOGY EGYPTIAN JOURNAL OF GENETICS AND CYTOLOGY REVISTA BRASILEIRA DE GENETICA ANNUAL REVIEW OF GENETICS JOURNAL OF CRANIOFACIAL GENETICS AND DEVELOPMENTAL BIOLOGY JOURNAL OF INHERITED METABOLIC DISEASE PRENATAL DIAGNOSIS ADVANCES IN GENETICS INCORPORATING MOLECULAR GENETIC MEDICINE CHEMICAL MUTAGENS-PRINCIPLES AND METHODS FOR THEIR DETECTION DNA-A JOURNAL OF MOLECULAR & CELLULAR BIOLOGY EVOLUTIONARY BIOLOGY TERATOGENESIS CARCINOGENESIS AND MUTAGENESIS TSITOLOGIYA I GENETIKA ADVANCES IN HUMAN GENETICS PROGRESS IN MEDICAL GENETICS GENETICS SELECTION EVOLUTION MOLECULAR BIOLOGY AND EVOLUTION SOMATIC CELL AND MOLECULAR GENETICS BIOTECHNOLOGY & GENETIC ENGINEERING REVIEWS EXPERIMENTAL AND CLINICAL IMMUNOGENETICS GENE ANALYSIS TECHNIQUES JOURNAL OF MOLECULAR AND APPLIED GENETICS JOURNAL OF NEUROGENETICS TRENDS IN GENETICS DISEASE MARKERS ANIMAL GENETICS GENETIC EPIDEMIOLOGY JOURNAL OF GENETICS
    • Ja1 Ja2 Ja3 Ja4 Jb1 Jb2 Jb3 Jb4 Jb5 Bibliogr.coupl. Ja2Journal map based on citation similarity Ja1 Ja4 Ja3
    • Radiology, Nuclear Medicine, & Medical Imaging111 journals, 2007-2010Citation-based network mapColors indicate the different subfields
    • Journal 1. Field = set of journals Defined by WoS or Scopus+ reference-based re-definition(expansion) of fields(e.g. Nature, Science)
    • Fa1 Fa2 Fa3 Fa4 Fb1 Fb2 Fb3 Fb4 Fb5 Bibliogr.coupl. Fa2Field map based on citation similarity Fa1 Fa4 Fa3
    • Science as a structure of 200related fieldsWorld map Size: World average Colors indicate the different fields
    • 29Leiden University
    • 30Delft University of Technology
    • Instead of using citations (references) tocalculate publication-similarity and to createcitation-based mapsone can in exactly the way mathematical wayuse concepts (keywords extracted from titlesand abstracts) to calculate publication-similarity and to createconcept-based maps
    • 2.Field = clusters of concept-related publications own definition: new, emerging, interdisciplinary fields, socio-economic themes Evaluation of universities’ ‘third mission’: Role of science for societal demands
    • Urban Studies 2006-2008Concept map, n=700Colors indicate the different subfields
    • Urban Studies 2006-2008Concept density mapn=700Colors indicate the different publication densities
    • ?Map 2015Map 2010 Knowledge dynamics described by density and flowMap 2005
    • Serious problem in impact assessment by citationsmentioned by more and more researchers:Normalization by an average citation density (FCSm)of the whole field might seriously prejudice groupsworking in a low density subfield How can we measure these within-field citation densities and determine how serious the problem is?
    • Combining concept-based maps with localcitation-density measurement
    • Clinical Neurology Neuro-surgery has a relatively low2005-2009 citation density within the field of clinical neurology Colors now indicate local citation density -->
    • Cardiovasc system 2005-2009Concept map, n=1,500Colors now indicate local citation density
    • 3. Leiden Ranking 2011-2012: new indicators
    • New and innovative features!No static tables but a user-friendly menu with (1) selection of universities (2) selection of performance dimension with specific indicators (3) selection of calculation method for the 500 largest universities worldwide
    • Proper normalization is absolutely crucial! Ranking of the 250 largest European universities by FCSm Rank of all 250 universities by FCSm 10.00 9.00 8.00 7.00 6.00 5.00 FCSm 4.00 3.00 2.00 1.00 0.00 0 50 100 150 200 250 300 r
    • www.leidenranking.com
    • (1) selection of universitiesRegion:* World* Regions (Europe, Asia, North America, SouthAmerica, Africa, Oceania)* CountriesBy number of universities covered in the ranking:*largest 100, 200, 300, 400, 500
    • (2) selection of performancedimension with specific indicators* impact: P, MCS, MNCS, PP(top10%)* collaboration: P, PP(collab), PP(int collab), MGCD, PP(long dist collab)indicators with stability intervals!
    • MNCS (earlier CPP/FCSm) is an averageand this is statistically not the best measure for askew distribution…So we move on to p-top10%this measures takes the whole distribution intoaccountNotice: dimension of MNCS is C/P dimension of p-top10% is n
    • (3) selection of calculation method* full or fractional assignment collaborativepublications* all WoS publications or only English language
    • Observations
    • General:* Large effect of all vs only-English publications for D and F* Large effect full vs fractional counting* One-paper-explosion effect* International collaboration
    • Stability intervals clearly show the dramaticinfluence of just one publication!Examples: Göttingen, Utrecht
    • P[2008]; C[2008-2011*] = 20,485
    • P[2009]; C[2009-2011*] = 2,856
    • The influence of Arts & Humanities onbibliometric university rankings ispractically non-existent….
    • Rank based onALL WoSpublications x Leave out non-English publications
    • Veryinfluential forD and F univ
    • EUR-50 / language: all / counting: full EUR-50 / language: Engl / counting: full P MNCS P MNCS 1 Univ Cambridge 25073 1.76 1 Univ Cambridge 25037 1.71 2 Univ Oxford 26161 1.71 2 Univ Oxford 26105 1.65 3 ETH Zurich 15890 1.63 3 ETH Zurich 15708 1.60 4 Imperial Coll London 22280 1.58 4 Imperial Coll London 22234 1.53 5 Univ Edinburgh 13318 1.57 5 Univ Edinburgh 13291 1.51 6 Utrecht Univ 18221 1.56 6 Utrecht Univ 18066 1.51 7 Erasmus Univ Rotterdam 12788 1.54 7 Univ Zurich 13840 1.50 8 Univ Bristol 12199 1.53 8 LMUniv München 15827 1.48 All <> Engl 9 Univ Coll London 26210 1.51 9 Univ Bristol 12170 1.4710 Univ Zurich 14492 1.50 10 Erasmus Univ Rotterdam 12738 1.4711 Kings Coll London 13502 1.46 11 Univ Coll London 26166 1.4512 VU Univ Amsterdam 12732 1.4613 Leiden Univ 12653 1.45 12 Univ Glasgow 13 VU Univ Amsterdam 10144 1.41 12697 1.40 MNCS:14 Univ Copenhagen 18654 1.43 14 Leiden Univ 12597 1.4015 Radboud Univ Nijmegen 12061 1.4316 Univ Amsterdam 15760 1.43 15 Heidelberg Univ 16 Kings Coll London 14552 1.40 13473 1.40 D, F17 Karolinska Inst 17054 1.41 17 Tech Univ München 11660 1.39 UK18 Aarhus Univ 13039 1.41 18 Radboud Univ Nijmegen 12000 1.3819 LMUniv München 17672 1.40 19 Univ Copenhagen 18633 1.3720 Katholieke Univ Leuven 19673 1.38 20 Univ Amsterdam 15689 1.3721 Lund Univ 15558 1.37 21 Univ Pierre & Marie Curie 19206 1.3722 Univ Manchester 18217 1.37 22 Karolinska Inst 17025 1.3523 Tech Univ München 12452 1.36 23 Univ Paris-Sud 11 13674 1.3524 Univ Groningen 12303 1.36 24 Aarhus Univ 13020 1.3525 Univ Southampton 10861 1.36 25 Univ Bonn 10035 1.34 26 Katholieke Univ Leuven 19540 1.34 27 Lund Univ 15528 1.3330 28 Univ Manchester 18194 1.32 29 Univ Groningen 12256 1.32 30 Univ Southampton 10843 1.32
    • Citation Impact German and French universities All versus English-only Publications 2,00 1,80 y = 1,08x 1,60 R2 = 0,81 1,40 1,20imp-eng 1,00 0,80 0,60 0,40 0,20 0,00 0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 1,60 1,80 2,00 imp-all Citation Impact Ranking German and French universities All versus English-only Publications 500 450 400 350 300r-eng 250 y = 1,02x - 38,31 2 R = 0,83 200 150 100 50 0 0 50 100 150 200 250 300 350 400 450 500 r-all
    • EUR-50 / language: Engl / counting: full EUR-50 / language: Engl / counting: frac P MNCS P MNCS 1 Univ Cambridge 25037 1.71 1 Univ Cambridge 14065 1.57 2 Univ Oxford 26105 1.65 2 ETH Zurich 8632 1.55 3 ETH Zurich 15708 1.60 3 Univ Oxford 13949 1.49 4 Imperial Coll London 22234 1.53 4 Utrecht Univ 9710 1.49 5 Univ Edinburgh 13291 1.51 5 Imperial Coll London 11567 1.41 6 Utrecht Univ 18066 1.51 6 Univ Bristol 6681 1.36 full <>frac 7 Univ Zurich 13840 1.50 7 Univ Coll London 13745 1.36 8 LMUniv München 15827 1.48 8 Univ Edinburgh 7142 1.32 9 Univ Bristol 12170 1.47 9 VU Univ Amsterdam 6494 1.31 MNCS:10 Erasmus Univ Rotterdam 12738 1.47 10 Erasmus Univ Rotterdam 6765 1.3111 Univ Coll London 26166 1.45 11 Univ Zurich 7754 1.2912 Univ Glasgow 10144 1.41 12 Univ Amsterdam 8104 1.29 All:13 VU Univ Amsterdam 12697 1.40 13 Kings Coll London 7249 1.2814 Leiden Univ 12597 1.40 14 Leiden Univ 6355 1.2715 Heidelberg Univ 14552 1.40 15 Univ Copenhagen 10157 1.2516 Kings Coll London 13473 1.40 16 Aarhus Univ 7069 1.24 Major changes17 Tech Univ München 11660 1.39 17 Univ Manchester 10427 1.2318 Radboud Univ Nijmegen 12000 1.38 18 Radboud Univ Nijmegen 6478 1.23 for the smaller19 Univ Copenhagen 18633 1.37 19 Tech Univ München 6672 1.22 universities20 Univ Amsterdam 15689 1.37 20 Univ Groningen 6967 1.2221 Univ Pierre & Marie Curie 19206 1.37 21 Univ Nottingham 6839 1.2022 Karolinska Inst 17025 1.35 22 Karolinska Inst 8573 1.2023 Univ Paris-Sud 11 13674 1.35 23 Univ Southampton 6017 1.1924 Aarhus Univ 13020 1.35 24 Katholieke Univ Leuven 10956 1.1925 Univ Bonn 10035 1.34 25 Univ Sheffield 6440 1.1926 Katholieke Univ Leuven 19540 1.34 26 LMUniv München 9585 1.1627 Lund Univ 15528 1.33 27 FAUniv Erlangen 5756 1.1628 Univ Manchester 18194 1.32 28 Univ Paris-Sud 11 6323 1.1529 Univ Groningen 12256 1.32 29 Univ Leeds 6627 1.1530 Univ Southampton 10843 1.32 30 Univ Pierre & Marie Curie 9309 1.14
    • EUR-100 / language: Engl / counting: frac P MNCS 1 Univ Göttingen 4776 2.04 2 EPF Lausanne 4790 1.69 3 Univ Cambridge 14046 1.53 4 ETH Zurich 8507 1.53 5 Utrecht Univ 9601 1.45 For the smaller universities 6 Univ Oxford 13919 1.44 7 Tech Univ Denmark 4287 1.43 fractional counting is quite influential, 8 Imperial Coll London 11547 1.37 particularly for Delft! 9 Univ Bristol 6671 1.3110 Univ Basel 4263 1.3111 Univ Coll London 13723 1.3112 Univ Zurich 7293 1.3013 Wageningen Univ 4590 1.3014 Univ Edinburgh 7128 1.2815 VU Univ Amsterdam16 LMUniv München 6471 8261 1.27 1.27 1517 Tech Univ München 6145 1.2618 Erasmus Univ Rotterdam 6735 1.2619 Univ Geneva 4971 1.2520 Univ Amsterdam 8059 1.2421 Delft Univ Technol 4531 1.2422 Kings Coll London 7236 1.2323 Leiden Univ 6321 1.2324 FAUniv Erlangen25 Univ Freiburg 5198 4768 1.22 1.22 2926 Univ Bern 4717 1.2127 Univ Pierre & Marie Curie 8416 1.2128 Karlsruhe Inst Technol 4362 1.2129 Univ Copenhagen 10145 1.2030 Aarhus Univ 7060 1.19 60!
    • EUR-100 / language: Engl / counting: frac EUR-100 / language: Engl / counting: frac PPtop P MNCS MNCS > p-top10%: P 10% 1 Univ Göttingen 4776 2.04 1 EPF Lausanne 4790 18.8% 2 EPF Lausanne 4790 1.69 Universities with rank 2 ETH Zurich 8507 17.6% 3 Univ Cambridge 14046 1.53 position dominated by 3 Univ Cambridge 14046 16.7% 4 ETH Zurich 8507 1.53 just 1 publication 4 Univ Oxford 13919 16.5% 5 Utrecht Univ 9601 1.45 5 Tech Univ Denmark 4287 15.9% 6 Univ Oxford 13919 1.44 change considerably in 6 Imperial Coll London 11547 15.2% 7 Tech Univ Denmark 4287 1.43 position 7 Univ Coll London 13723 14.8% 8 Imperial Coll London 11547 1.37 8 Univ Edinburgh 7128 14.4% 9 Univ Bristol 6671 1.31 9 Wageningen Univ 4590 14.3%10 Univ Basel 4263 1.31 10 Univ Zurich 7293 14.3%11 Univ Coll London 13723 1.31 11 Univ Bristol 6671 14.2%12 Univ Zurich 7293 1.30 12 Erasmus Univ Rotterdam 6735 14.2%13 Wageningen Univ 4590 1.30 13 VU Univ Amsterdam 6471 14.1%14 Univ Edinburgh 7128 1.28 14 Univ Basel 4263 14.1%15 VU Univ Amsterdam Ludwig-Maximilians-Univ 6471 1.27 15 Univ Geneva 4971 13.9%16 Münche 8261 1.27 16 LMUniv München 8261 13.8%17 Tech Univ München 6145 1.26 17 Utrecht Univ 9601 13.6%18 Erasmus Univ Rotterdam 6735 1.26 18 Tech Univ München 6145 13.5%19 Univ Geneva 4971 1.25 19 Leiden Univ 6321 13.3%20 Univ Amsterdam 8059 1.24 20 Univ Amsterdam 8059 13.2%21 Delft Univ Technol 4531 1.24 21 Univ Freiburg 4768 13.1%22 Kings Coll London 7236 1.23 22 Kings Coll London 7236 13.1%23 Leiden Univ 6321 1.23 23 Delft Univ Technol 4531 12.9%24 FAUniv Erlangen 5198 1.22 24 Aarhus Univ 7060 12.9%25 Univ Freiburg 4768 1.22 25 Univ Paris-Sud 11 5866 12.9%26 Univ Bern 4717 1.21 26 FAUniv Erlangen 5198 12.8%27 Univ Pierre & Marie Curie 8416 1.21 27 Univ Copenhagen 10145 12.8%28 Karlsruhe Inst Technol 4362 1.21 28 Karlsruhe Inst Technol 4362 12.7% 62 !!29 Univ Copenhagen 10145 1.20 29 Univ Pierre & Marie Curie 8416 12.7%30 Aarhus Univ 7060 1.19 30 Univ Glasgow 5262 12.6%
    • 500 largest universities worldwide P[2005-2009], C[2005-2010] 0.3 0.25 0.2pp(top10%) 0.15 y = 0.14x - 0.04 R2 = 0.97 0.1 0.05 0 0 0.5 1 1.5 2 2.5 MNCS-engl
    • EUR-50 / language: Engl / counting: frac P PPint collab 1 ETH Zurich 8507 36.5% 2 Univ Zurich 7293 36.0% 3 Karolinska Inst 8561 35.4% 4 Univ Paris-Sud 11 5866 34.3% 5 Katholieke Univ Leuven 10872 33.9% 6 Aarhus Univ 7060 33.1% 7 Univ Pierre & Marie Curie 8416 32.6% 8 Univ Copenhagen 10145 32.2% 9 Lund Univ10 Univ Oslo 8473 6864 32.0% 31.4% Collaboration and, more specifically,11 Univ Helsinki 7988 31.3% international collaboration:12 Univ Oxford 13919 31.1%13 Uppsala Univ14 Imperial Coll London 6928 11547 30.9% 30.5% * no correlation with impact15 Leiden Univ 6321 30.1%16 Univ Cambridge 14046 29.2% * weak correlation with MGCD (see Leiden17 VU Univ Amsterdam 6471 28.8% Ranking website)18 Univ Coll London 13723 28.6%19 Humboldt-Univ Berlin 6441 28.5%20 Univ Amsterdam 8059 28.4%21 Charles Univ Prague 5396 28.2%22 Univ Edinburgh 7128 27.9%23 Ghent Univ 8935 27.8%24 Tech Univ München 6145 27.7%25 LMUniv München 8261 27.6%
    • Correlation of p-int-collab with MGCD for the 100 largest European universities 1.000 0.56p-int-collab y = 0.01x 2 R = 0.47 0.100 1000 10000 MGCD
    • Correlation of p-int-coll with MGCD for Australia 1,00p-int-coll 0,92 y = 0,00x 2 R = 0,94 0,10 1000,00 10000,00 MGCD
    • 4. Evaluation tools related to the LeidenRanking
    • Built on the basic data of the Leiden Ranking 2011:Leiden Benchmark Analysis:* all indicators of the ‘total’ university ranking are available for 30main (NOWT) fields of science* their trends in the past 10 years* world rank by indicator and by main fieldAll this in comparison with 20 other universities by choice* all indicators for each field within each main field (total 200 fields)with distinction between fields above and below university average
    • Current and recent benchmark projectsManchester, Leiden, Heidelberg,Rotterdam, Copenhagen, Zürich, Lisbon UNL, Amsterdam UvA, Amsterdam VU, Southampton Gent, Antwerp, Brussels VUB, UC London, Aarhus, Oslo
    • Output & impact compared to benchmark universities 2002 - 2007 CHEMISTRY Groningen 2 Eindhoven Cambridge Twente UvA Nijmegen Freiburg 1.5 TU Delft Utrecht Geneve Lunds VU Adam Edinburgh LMU Munchen Oxford Wageningen Helsinki Paris VICPP/FCSm Strasbourg I Heidelberg KU Leuven Leiden Zurich Paris XI UC London Milano 1 Karolinska 0.5 0 0 500 1000 1500 2000 2500 TOTAL PUBLICATIONS
    • Output and impact 2002 - 2007 SOCIAL SCIENCES Wageningen 1,4 Edinburgh LMU Munchen Maastricht Freiburg UC London Cambridge 1,2 Zurich UvA Oxford Leiden Utrecht Erasmus Eindhoven KU Leuven VU Adam Tilburg 1 Twente Groningen Karolinska Lund Helsinki Geneve 0,8 NijmegenCPP/FCSm Paris VI Heidelberg Paris XI TU Delft 0,6 Milano 0,4 0,2 0 0 200 400 600 800 1000 1200 1400 TOTAL PUBLICATIONS
    • FIGURE VII.12: RESEARCH AND IMPACT PROFILE COMPARISON CHART 2001 - 2008 UNIV OSLO LUNDS UNIV ONCOLOGY 1.01 1.17 BIOCHEM&MOL BIOL 1.06 0.94 IMMUNOLOGY 0.84 0.87 CARD&CARDIOV SYS 1.62 0.97 PUBL ENV OCC HLT 1.20 1.10 ASTRON&ASTROPH 1.25 1.12 NEUROSCIENCES 1.20 1.25 PSYCHIATRY 1.10 0.70 HEMATOLOGY 1.17 1.04 CELL BIOLOGY 1.11 0.98 MEDICINE,GEN&INT 4.70 2.44 CLIN NEUROLOGY 1.17 2.04ENDOCRIN&METABOL 1.19 1.46 PERIPHL VASC DIS 1.37 1.00 CHEM,PHYSICAL 0.92 1.25 SURGERY 1.55 1.33 GEOSC,MULTIDISC 1.25 1.98 ECOLOGY 1.27 1.31DENT,ORAL SURG&M 1.15 0.98 PEDIATRICS 1.42 1.17 GENETICS&HEREDIT 1.44 2.12OBSTETRICS&GYNEC 1.20 1.25GASTROENTEROLOGY 1.41 1.13 PHYSICS,COND MAT 1.08 1.20PHARMACOL&PHARMA 1.14 1.01 PHYSICS,MULTIDIS 1.45 2.00 NUTRITION&DIET 1.52 1.34 RAD,NUCL MED IM 0.83 0.88 PATHOLOGY 1.15 1.10 MEDICINE,RES&EXP 0.91 1.95 7% 5% 3% 1% 1% 3% 5% 7% Percentage of Total Publication OutputIMPACT: LOW AVERAGE HIGH
    • Leiden University Major field field P(2005-9) MNCSP>100 CLINICAL MEDICINE MEDICINE, GENERAL & INTERNAL 291.2 3.03rank by PHYSICS AND ASTRONOMY PHYSICS, MULTIDISCIPLINARY 192.5 2.65MNCS PHYSICS AND ASTRONOMY PHYSICS, CONDENSED MATTER 204.0 2.10 CLINICAL MEDICINE RESPIRATORY SYSTEM 122.7 1.91 CLINICAL MEDICINE RHEUMATOLOGY 349.0 1.90 CLINICAL MEDICINE CARDIAC & CARDIOVASC SYSTEMS 488.5 1.84 BIOL SCI: HUMANS MEDICINE, RESEARCH & EXPER 100.0 1.81 CLINICAL MEDICINE SURGERY 207.9 1.73 CLINICAL MEDICINE UROLOGY & NEPHROLOGY 158.0 1.67 MOLECULAR BIOL & BIOCHEM BIOTECH & APPLIED MICROBIOL 106.8 1.66 SOCIAL SC RELATED TO MED PUBLIC, ENVIRONM & OCC HEALTH 111.6 1.65 PHYSICS AND ASTRONOMY ASTRONOMY & ASTROPHYSICS 814.5 1.65 BIOL SCI: HUMANS MICROBIOLOGY 179.3 1.58 CLINICAL MEDICINE GENETICS & HEREDITY 394.9 1.57 CLINICAL MEDICINE PERIPHERAL VASCULAR DISEASE 268.0 1.51 Leiden average MNCS = 1.45
    • It appears that the medical fields benefitmore and the engineering fields lessfrom the full countingThis will be very influential in the relativeposition of the engineering fields within auniversity, particularly in performanceanalyses
    • What !!??Shanghai and THES??Use the Leiden Rankingfrom now on!
    • Thank you for your attentionmore information: www.cwts.leidenuniv.nl