New developments in bibliometric methods:      evaluation, mapping, ranking                   Ton van Raan       Atelier B...
Leiden University         oldest in the Netherlands, 1575European League of Research Universities (LERU)               Wit...
Contents of this presentation:* Bibliometric methodology:  • impact  • maps* Leiden Ranking 2011-2012: new indicators* Eva...
Total publ universe                                                                                                       ...
Aliens from GalaxyM35-245 approach planetEarth. They have accessto the Web of Science…..
pa1           pa2        pa3          pa4                                       citing p <> cited p  pb1           pb2    ...
Primary network is a structure of different items(e.g., citing publ <> cited publ; citing publ <> concepts)In-degree prima...
1. Bibliometric methodology: impact
Classement Leiden Ranking
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Classement Leiden Ranking

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

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  1. 1. 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
  2. 2. 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
  3. 3. Contents of this presentation:* Bibliometric methodology: • impact • maps* Leiden Ranking 2011-2012: new indicators* Evaluation tools related to the Leiden Ranking
  4. 4. 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
  5. 5. Aliens from GalaxyM35-245 approach planetEarth. They have accessto the Web of Science…..
  6. 6. 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
  7. 7. 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
  8. 8. 1. Bibliometric methodology: impact
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