classement leiden ranking
TRANSCRIPT
New developments in bibliometric methods: evaluation, mapping, ranking
Ton van Raan
Atelier Bibliométrie de l’URFIST de Paris23 mars 2012 – CNAM
Center for Science and Technology Studies (CWTS)Leiden University
2
Leiden University oldest in the Netherlands, 1575
European League of Research Universities (LERU)Within world top-100
12 Nobel Prizes
Leiden, historic city (2th, 11th C.), strong cultural (arts, painting) & scientific tradition
one of the largest science parks in EU
Contents of this presentation:
* Bibliometric methodology: • impact • maps
* Leiden Ranking 2011-2012: new indicators* Evaluation tools related to the Leiden Ranking
4
WoS/Scopus sub-universejournal articles only,> 1,000,000p/y
VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002
Truncation of Power Law Behavior in “Scale-Free” Network Modelsdue to Information Filtering
Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and Luís A. Nunes Amaral11 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,Università di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy
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 conditions—that is, when the nodes can process information about only a subset of the exist ing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling 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 t he 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
There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide 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 dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. It is therefore important to know the global structure of the network as well as its precise distr ibution 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 the number 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-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes wit h the largest 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 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 based on what information it has about the state of the network. The preferential attachment mechanism then comes into p lay as nodes with a larger degree are more likely to become known.
VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002
Truncation of Power Law Behavior in “Scale-Free” Network Modelsdue to Information Filtering
Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and Luís A. Nunes Amaral11 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,Università di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy
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 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 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/PhysRevLet t.88.138701 PACS numbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc
There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide 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 dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. 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 the number 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-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the fo llowing way: New nodes want to connect to the existing nodes with the largest 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 is not plausible that a new node will know the degrees of all ex isting nodes, so a new node must make a decision on which node to connect with 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.
VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002
Truncation of Power Law Behavior in “Scale-Free” Network Modelsdue to Information Filtering
Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and Luís A. Nunes Amaral11 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,Università di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy
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 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 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
There is a great deal of current interest in understanding the s tructure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is criti cal in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the node s with the largest number of links play an important role on the dynamics of the system. 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 W WW have scale-free properties; that is, the number of incoming links and the number 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-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the fo llowing way: New nodes want to connect to the existing nodes with the largest 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 is not plausible that a new node will know the degrees of all ex isting nodes, so a new node must make a decision on which node t o connect with based on what information it has abou t 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.
VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002
Truncation of Power Law Behavior in “Scale-Free” Network Modelsdue to Information Filtering
Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and Luís A. Nunes Amaral11 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,Università di Roma “La Sapienza,” Piazzale Aldo Moro 2, I-00185, Roma, Italy
3 CEA-Service de Physique de la Matière Condens ée, 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 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 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 agreem ent.
DOI: 10.1103/PhysRevLett.88.138701 PACS numbers: 89.20.Hh, 84.35.+i, 89.75.Da, 89.75.Hc
There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide 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 dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. 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 the number of outgoing links at a given node have distribut ions that decay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes with the largest number o f 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 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 t o connect with 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.
Refs > non-WoS
Total publ universenon-WoS publ:
Books
Book chapters
Conf. proc.
Reports
Non-WoS journals
Aliens from Galaxy M35-245 approach planet Earth. They have access to the Web of Science…..
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Co-citation-network Bibliogr.coupl.network
Primary Citation Network
cited p <> cited p
citing p <> citing p
citing p <> cited p
Primary network is a structure of different items (e.g., citing publ <> cited publ; citing publ <> concepts)
In-degree primary network => received citations > impact
Secondary network is a structure of similar items (e.g., citing publ<> citing publ; concepts <> concepts)
Similarity in the secondary networks => co-occurrence map
1. Bibliometric methodology: impact
VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002
Truncation of Power Law Behavior in “Scale-Free”Network Modelsdue to Information Filtering
Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and LuísA. NunesAmaral11 Center for Polymer Studies and Department of Physics, Boston Uni versity, Boston, Massachusetts 02215
2 Dipartimento di Fisica, INFM UdR, and INFM Center for Statistical Mechanics and Complexity,Universitàdi Roma “La Sapienza,”PiazzaleAldo Moro 2, I-00185, Roma, Italy
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 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 form, i.e., that it decays as a power law with an exponential tr uncation 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
There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide 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 dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on thedynamics of the system. 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 the number 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-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment” [10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes with the largest 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 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 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.
VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002
Truncation of Power Law Behavior in “Scale-Free”Network Modelsdue to Information Filtering
Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and LuísA. NunesAmaral11 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,”PiazzaleAldo Moro 2, I-00185, Roma, Italy
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 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 incominglinks to a node follows a universal scaling 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
There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide 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 dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on thedynamics of the system. 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 the number of outgoing links at a given node have distributions thatdecay with power law tails [4–6]. It has been proposed [9] that the scale-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment”[10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes with the largest 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 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 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.
VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002
Truncation of Power Law Behavior in “Scale-Free”Network Modelsdue to Information Filtering
Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and LuísA. NunesAmaral11 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,UniversitàdiRoma “La Sapienza,”PiazzaleAldo Moro 2, I-00185, Roma, Italy
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 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 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
There is a great deal of current interest in understanding the s tructure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is criti cal in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. It is therefore important to know the global structure o f 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 the number 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-free structure of the Internet and the WWW may be explained by a mech anism referred to as “preferential attachment”[10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the fo llowing way: New nodes want to connect to the existing nodes with the largest 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 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 t o connect with 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.
VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002
Truncation of Power Law Behavior in “Scale-Free”Network Modelsdue to Information Filtering
Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and LuísA. NunesAmaral11 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,UniversitàdiRoma “La Sapienza,”Piazzale Aldo Moro 2, I-00185, Roma, Italy
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 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 form, i.e., that it decays as a power law with an exponential tr uncation controlled not only by the system size but also by a feature not previously considered, the subset of t he 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
There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1 –3], such as the World Wide 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 dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. 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 the number 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-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment”[10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes wit h the largest 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 is not plausible that a new node will know the degrees of all ex isting nodes, so a new node must make a decision on which node to connect with 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.
VOLUME 88, Number 13 PHYSICAL REVIEW LETTERS 1 April 2002
Truncation of Power Law Behavior in “Scale-Free”Network Modelsdue to Information Filtering
Stefano Mossa,1,2 Marc Barthélémy,3 H. Eugene Stanley,1 and LuísA. NunesAmaral11 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,”PiazzaleAldo Moro 2, I-00185, Roma, Italy
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 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 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
There is a great deal of current interest in understanding the structure and growth mechanisms of global networks [1–3], such as the World Wide Web (WWW) [4,5] and the Internet [6]. Network structure is criti cal in many contexts such as Internet attacks [2], spread of an Email virus [7], or dynamics of human epidemics [8]. In all these problems, the nodes with the largest number of links play an important role on the dynamics of the system. 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 the number 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-free structure of the Internet and the WWW may be explained by a mechanism referred to as “preferential attachment”[10] in which new nodes link to existing nodes with a probability proportional to the number of existing links to these nodes. Here we focus on the stochastic character of the preferential attachment mechanism, which we understand in the following way: New nodes want to connect to the existing nodes with the largest 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 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 t o connect with 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.
Weight?
networks leading, possibly, to different dynamics, e.g., for the initiation and spread of epidemics.In the context of network growth, the impossibility of knowing the degrees of all the nodes comprising the network due to the filtering process—and, hence, the inability to make the optimal, rational, choice—is not altogether unlike the “bounded rationality” concept of Simon [17].Remarkably, it appears that, for the description of WWW growth, the preferential attachment mechanism, originally proposed by Simon [10], must be modified along the lines of another concept also introduced by him—bounded rationality [17].
We thank R. Albert, P. Ball, A.-L. Barabási, M. Buchanan, J. Camacho, and R. Guimerà for stimulating discussions and helpful suggestions. We are especially grateful to R. Kumar for sharing his data. We thank NIH/NCRR (P41 RR13622) and NSF for support.
[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. 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 (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, 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).[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).[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).[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).[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.[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.[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).
Citing Publications
Cited Publications
From other disciplinesFrom emerging fieldsFrom research devoted to societal, economical and technological problemsFrom industryFrom international top-groupsThese all f(t)!! > Sleeping Beauties
1 2 3 4 5 6 7
a b c d e f g
Fig. 1: Citing and cited publications
Intermezzo: statistical properties of the primary network
Out-degree (all)= 1
In-degree (b) = 5
Large differences among research fields in citation density
Citation counts must always be normalized to field-specific values
and therefore:
H-index is not normalized and inconsistent
500
Dept. Radiology CPP = 6
Radiology journals JCSm = 4
CPP/JCSm=1.5
P2007-2010
150,000
Radiology field FCSm = 3
CPP/FCSm = 2
C2007-2010
3000 450,000
25,000
100,000
13
• Two normalization mechanisms:
ci: actual number of citations of publication iei: expected number of citations of publication i
• Value of 1 indicates performance at world average
n
i i
n
i i
n
i i
n
i i
e
c
ne
nc
1
1
1
1CPP/FCSm
n
i i
i
ec
n 1
1MNCS
Applied research, engineering, social sciences
Basic research in natural and medical science
high FCSm
Δ Citation density ~20 !
high FCSm, but low JCSm
low FCSm
low FCSm, but high JCSm
High CPP
low CPP
PHYSICS
0
5000
10000
15000
20000
25000
30000
35000
Cits91 Cits92 Cits93 Cits94 Cits95 Cits96 Cits97 Cits98 Cits99 Cits00 Cits01 Cits02
Publications from 1991,….1995
time lag & citation window
* SOCIOLOGY
0
500
1000
1500
2000
2500
Cits91 Cits92 Cits93 Cits94 Cits95 Cits96 Cits97 Cits98 Cits99 Cits00 Cits01 Cits02
Again: never use journal impact factors!!
University
Departments
Fields
‘bottom-up’ analysis: input data (assignment of researchers to departments) necessary;> Detailed research performance analysis of a university by department
‘top-down’ analysis: field-structure is imposed to university;> Broad overview analysis of a university by field
H = 49
H = 27
P C+sc
Dept of Radiology, UMC
1997 - 2009 1,045 20,552
1997 - 2000 196 6161998 - 2001 213 9381999 - 2002 243 1,1512000 - 2003 231 1,5902001 - 2004 221 1,5762002 - 2005 306 2,0972003 - 2006 358 2,5162004 - 2007 417 3,3682005 - 2008 492 4,2542006 - 2009 497 3,986
CPP Pnc
16.73 17%
2.32 51%3.43 38%3.63 39%5.61 36%5.94 29%5.72 40%5.88 35%6.82 35%7.10 30%6.42 30%
J CSm FCSm
13.63 9.84
2.16 1.923.30 2.604.00 3.004.78 3.144.34 3.033.94 2.793.71 2.654.37 3.035.08 3.735.08 3.52
CPP/ CPP/ J CSm/ J CSm FCSm FCSm
1.23 1.70 1.36
1.07 1.21 1.091.04 1.32 1.230.91 1.21 1.291.17 1.79 1.471.37 1.96 1.391.45 2.05 1.371.58 2.21 1.361.56 2.25 1.401.40 1.90 1.331.26 1.82 1.41
SelfCit
15%
26%22%23%18%17%17%16%16%18%20%
Crown indicator
Impact trend of two depts. Radiology (UMC A and B)
0.00
0.50
1.00
1.50
2.00
2.50
3.00
1997 -2000
1998 -2001
1999 -2002
2000 -2003
2001 -2004
2002 -2005
2003 -2006
2004 -2007
2005 -2008
2006 -2009
years
CPP/FCSm
above
below international level
P(2006-9)=561: in which fields?
RESEARCH PROFILEPUBLICATIONS AND IMPACT PER FIELD
2006 - 2009
Radiology UMC
0 5 10 15 20 25 30 35
RAD,NUCL MED I M (1.63)
CARD&CARDIOV SYS (2.24)
SURGERY (1.71)
CLI N NEUROLOGY (2.15)
ONCOLOGY (1.55)
PERIPHL VASC DI S (1.20)
PEDI ATRI CS (1.09)
MEDICI NE,GEN&INT (4.02)
NEUROSCI ENCES (2.71)
GASTROENTEROLOGY (0.59)
HEMATOLOGY (2.06)
ENDOCRIN&METABOL(1.11)
ORTHOPEDICS (1.17)
UROLOGY&NEPHROL (3.22)
OPHTHALMOLOGY (0.78)
NEUROI MAGING (1.21)
ENG,BIOMEDI CAL (0.54)
FIELD(CPP/ FCSm)
Share of the output (%)
IMPACT: LOW AVERAGE HIGH
Research ProfileOutput (P) and Impact (CPP/FCSm) 2006 - 2009
Dept Radiology UMC
FIGURE IV.1a:
IMPACT PROFILE:PUBLICATIONS AND IMPACT PER SUBFIELD
1992 - 2000
Erkrankungen des Nervensystems
0% 5% 10% 15% 20% 25% 30% 35% 40%
NEUROSCIENCES (2.28)
BIOCH & MOL BIOL (2.00)
CELL BIOLOGY (1.96)
DEVELOPMENT BIOL (1.33)
MULTIDISCIPL SC (2.84)
GENETICS & HERED (2.70)
PHYSIOLOGY (1.74)
CLIN NEUROLOGY (1.58)
PHARMACOL & PHAR (3.94)
ONCOLOGY (1.60)
BIOLOGY (2.06)
ANAT & MORPHOL (2.15)
ENDOCRIN & METAB (1.81)
PATHOLOGY (2.11)
UROLOGY & NEPHRO (1.30)
BIOPHYSICS (1.97)
IMMUNOLOGY (1.84)
MEDICINE, RES (1.88)
ZOOLOGY (4.51)
OPHTHALMOLOGY (2.21)
SUBFIELD (CPP/FCSm)
Relative share of impact
IMPACT: LOW AVERAGE HIGH
FIGURE III.1:
COGNITIVE ORIENTATION:PUBLICATIONS AND IMPACT PER SUBFIELD
1992 - 2000
Erkrankungen des Nervensystems
0% 10% 20% 30% 40% 50% 60%
NEUROSCIENCES (2.70)
BIOCH & MOL BIOL (1.92)
CELL BIOLOGY (2.74)
MULTIDISCIPL SC (4.33)
DEVELOPMENT BIOL (1.32)
SUBFIELD (CPP/FCSm)
Percentage of Publications
IMPACT: LOW AVERAGE HIGH
CPP/ FCSm
CPP/ FCSm
Diseases of the NeurosystemDept Output in Fields Dept Impact from Fields
Knowledge use by very high impact groups
Neurosc 2.70Biochem 1.92Cell Biol 2.74multidisc 4.33Devel Biol 1.32
Neurosc 2.28Biochem 2.00Cell Biol 1.96Devel Biol 1.33multidisc 2.84Genetics 2.70Physiol 1.74Clin Neurol 1.58Pharmacol 3.94Oncology 1.60Biology 2.06Anat Morph 2.15Endocrinol 1.81Pathology 2.11Urology 1.30Biophysics 1.97
Immunol 1.84Res Medic 1.88Zoology 4.51Opthalmol 2.21
2. Bibliometric methodology: maps
Major field
fields
Main field: Medical & Life Sciences BI OMEDICAL SCI ENCES
ANATOMY & MORPHOLOGY IMMUNOLOGY INTEGRATIVE & COMPLEMENTARY MEDICINE MEDICAL LABORATORY TECHNOLOGY MEDICINE, RESEARCH & EXPERIMENTAL NEUROIMAGING NEUROSCIENCES PHARMACOLOGY & PHARMACY PHYSIOLOGY RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING TOXICOLOGY VIROLOGY
ACTA GENETICAE MEDICAE ET GEMELLOLOGIAEAMERICAN JOURNAL OF HUMAN GENETICSAMERICAN JOURNAL OF MEDICAL GENETICSANIMAL BLOOD GROUPS AND BIOCHEMICAL GENETICSANNALES DE GENETIQUEANNALES DE GENETIQUE ET DE SELECTION ANIMALEANNALS OF HUMAN GENETICSATTI ASSOCIAZIONE GENETICA ITALIANABEHAVIOR GENETICSBIOCHEMICAL GENETICSCANADIAN JOURNAL OF GENETICS AND CYTOLOGYCANCER GENETICS AND CYTOGENETICSCARYOLOGIACHROMOSOMACLINICAL GENETICSCURRENT GENETICSCYTOGENETICS AND CELL GENETICSCYTOLOGIADEVELOPMENTAL GENETICSENVIRONMENTAL MUTAGENESISEVOLUTIONGENEGENETICAGENETICA POLONICAGENETICAL RESEARCHGENETICSGENETIKAHEREDITASHEREDITYHUMAN BIOLOGYHUMAN GENETICSHUMAN HEREDITYIMMUNOGENETICSINDIAN JOURNAL OF GENETICS AND PLANT BREEDINGJAPANESE JOURNAL OF GENETICSJAPANESE JOURNAL OF HUMAN GENETICSJOURNAL DE GENETIQUE HUMAINEJOURNAL OF HEREDITYJOURNAL OF IMMUNOGENETICSJOURNAL OF MEDICAL GENETICSJOURNAL OF MENTAL DEFICIENCY RESEARCHJOURNAL OF MOLECULAR EVOLUTIONMOLECULAR & GENERAL GENETICSMUTATION RESEARCHPLASMIDSILVAE GENETICATHEORETICAL AND APPLIED GENETICSTHEORETICAL POPULATION BIOLOGYEGYPTIAN JOURNAL OF GENETICS AND CYTOLOGYREVISTA BRASILEIRA DE GENETICAANNUAL REVIEW OF GENETICSJOURNAL OF CRANIOFACIAL GENETICS AND DEVELOPMENTAL BIOLOGYJOURNAL OF INHERITED METABOLIC DISEASEPRENATAL DIAGNOSISADVANCES IN GENETICS INCORPORATING MOLECULAR GENETIC MEDICINECHEMICAL MUTAGENS-PRINCIPLES AND METHODS FOR THEIR DETECTIONDNA-A JOURNAL OF MOLECULAR & CELLULAR BIOLOGYEVOLUTIONARY BIOLOGYTERATOGENESIS CARCINOGENESIS AND MUTAGENESISTSITOLOGIYA I GENETIKAADVANCES IN HUMAN GENETICSPROGRESS IN MEDICAL GENETICSGENETICS SELECTION EVOLUTIONMOLECULAR BIOLOGY AND EVOLUTIONSOMATIC CELL AND MOLECULAR GENETICSBIOTECHNOLOGY & GENETIC ENGINEERING REVIEWSEXPERIMENTAL AND CLINICAL IMMUNOGENETICSGENE ANALYSIS TECHNIQUESJOURNAL OF MOLECULAR AND APPLIED GENETICSJOURNAL OF NEUROGENETICSTRENDS IN GENETICSDISEASE MARKERSANIMAL GENETICSGENETIC EPIDEMIOLOGYJOURNAL OF GENETICS
journals
Citation relations between journals are used to define fields of science
Ja1 Ja2 Ja3 Ja4
Jb1 Jb2 Jb3 Jb4 Jb5
Ja2
Ja1 Ja4
Ja3
Bibliogr.coupl.
Journal map based on citation similarity
Colors indicate the different subfields
Radiology, Nuclear Medicine, & Medical Imaging 111 journals, 2007-2010Citation-based network map
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
Fa2
Fa1 Fa4
Fa3
Bibliogr.coupl.
Field map based on citation similarity
Size: World average
Science as a structure of 200 related fieldsWorld map
Colors indicate the different fields
29Leiden University
30Delft University of Technology
Instead of using citations (references) to calculate publication-similarity and to create citation-based maps
one can in exactly the way mathematical way use concepts (keywords extracted from titles and abstracts) to calculate publication-similarity and to create concept-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=700
Colors indicate the different subfields
Urban Studies 2006-2008Concept density map n=700Colors indicate the different publication densities
Map 2005
Map 2010 Knowledge dynamics described by density and flow
?Map 2015
Serious problem in impact assessment by citations mentioned by more and more researchers:
Normalization by an average citation density (FCSm) of the whole field might seriously prejudice groups working 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 local citation-density measurement
Clinical Neurology 2005-2009
Colors now indicate local citation density -->
Neuro-surgery has a relatively low citation density within the field of clinical neurology
Cardiovasc system 2005-2009Concept map, n=1,500
Colors 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 methodfor the 500 largest universities worldwide
Proper normalization is absolutely crucial!
Rank of all 250 universities by FCSm
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
0 50 100 150 200 250 300r
FCSm
Ranking of the 250 largest European universities by FCSm
www.leidenranking.com
(1) selection of universities
Region:
* World * Regions (Europe, Asia, North America, South America, Africa, Oceania) * Countries
By number of universities covered in the ranking:
*largest 100, 200, 300, 400, 500
(2) selection of performance dimension 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 average
and this is statistically not the best measure for a skew distribution…
So we move on to p-top10%
this measures takes the whole distribution into account
Notice: dimension of MNCS is C/Pdimension of p-top10% is n
(3) selection of calculation method
* full or fractional assignment collaborative publications* 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 dramatic influence 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 on bibliometric university rankings is practically non-existent….
Leave out non-English publications x
Rank based on ALL WoS publications
Very influential for D and F univ
EUR-50 / language: all / counting: fullP MNCS
1 Univ Cambridge 25073 1.762 Univ Oxford 26161 1.713 ETH Zurich 15890 1.634 Imperial Coll London 22280 1.585 Univ Edinburgh 13318 1.576 Utrecht Univ 18221 1.567 Erasmus Univ Rotterdam 12788 1.548 Univ Bristol 12199 1.539 Univ Coll London 26210 1.51
10 Univ Zurich 14492 1.5011 King's Coll London 13502 1.4612 VU Univ Amsterdam 12732 1.4613 Leiden Univ 12653 1.4514 Univ Copenhagen 18654 1.4315 Radboud Univ Nijmegen 12061 1.4316 Univ Amsterdam 15760 1.4317 Karolinska Inst 17054 1.4118 Aarhus Univ 13039 1.4119 LMUniv München 17672 1.4020 Katholieke Univ Leuven 19673 1.3821 Lund Univ 15558 1.3722 Univ Manchester 18217 1.3723 Tech Univ München 12452 1.3624 Univ Groningen 12303 1.3625 Univ Southampton 10861 1.36
EUR-50 / language: Engl / counting: fullP MNCS
1 Univ Cambridge 25037 1.712 Univ Oxford 26105 1.653 ETH Zurich 15708 1.604 Imperial Coll London 22234 1.535 Univ Edinburgh 13291 1.516 Utrecht Univ 18066 1.517 Univ Zurich 13840 1.508 LMUniv München 15827 1.489 Univ Bristol 12170 1.47
10 Erasmus Univ Rotterdam 12738 1.4711 Univ Coll London 26166 1.4512 Univ Glasgow 10144 1.4113 VU Univ Amsterdam 12697 1.4014 Leiden Univ 12597 1.4015 Heidelberg Univ 14552 1.4016 King's Coll London 13473 1.4017 Tech Univ München 11660 1.3918 Radboud Univ Nijmegen 12000 1.3819 Univ Copenhagen 18633 1.3720 Univ Amsterdam 15689 1.3721 Univ Pierre & Marie Curie 19206 1.3722 Karolinska Inst 17025 1.3523 Univ Paris-Sud 11 13674 1.3524 Aarhus Univ 13020 1.3525 Univ Bonn 10035 1.3426 Katholieke Univ Leuven 19540 1.3427 Lund Univ 15528 1.3328 Univ Manchester 18194 1.3229 Univ Groningen 12256 1.3230 Univ Southampton 10843 1.32
30
All <> Engl
MNCS:
D, FUK
Citation Impact German and French universities
All versus English-only Publications
y = 1,08xR2 = 0,81
0,00
0,20
0,40
0,60
0,80
1,00
1,20
1,40
1,60
1,80
2,00
0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 1,60 1,80 2,00imp-all
imp-eng
Citation Impact Ranking German and French universities
All versus English-only Publications
y = 1,02x - 38,31R2 = 0,83
0
50
100
150
200
250
300
350
400
450
500
0 50 100 150 200 250 300 350 400 450 500r-all
r-eng
EUR-50 / language: Engl / counting: fullP MNCS
1 Univ Cambridge 25037 1.712 Univ Oxford 26105 1.653 ETH Zurich 15708 1.604 Imperial Coll London 22234 1.535 Univ Edinburgh 13291 1.516 Utrecht Univ 18066 1.517 Univ Zurich 13840 1.508 LMUniv München 15827 1.489 Univ Bristol 12170 1.47
10 Erasmus Univ Rotterdam 12738 1.4711 Univ Coll London 26166 1.4512 Univ Glasgow 10144 1.4113 VU Univ Amsterdam 12697 1.4014 Leiden Univ 12597 1.4015 Heidelberg Univ 14552 1.4016 King's Coll London 13473 1.4017 Tech Univ München 11660 1.3918 Radboud Univ Nijmegen 12000 1.3819 Univ Copenhagen 18633 1.3720 Univ Amsterdam 15689 1.3721 Univ Pierre & Marie Curie 19206 1.3722 Karolinska Inst 17025 1.3523 Univ Paris-Sud 11 13674 1.3524 Aarhus Univ 13020 1.3525 Univ Bonn 10035 1.3426 Katholieke Univ Leuven 19540 1.3427 Lund Univ 15528 1.3328 Univ Manchester 18194 1.3229 Univ Groningen 12256 1.3230 Univ Southampton 10843 1.32
EUR-50 / language: Engl / counting: fracP MNCS
1 Univ Cambridge 14065 1.572 ETH Zurich 8632 1.553 Univ Oxford 13949 1.494 Utrecht Univ 9710 1.495 Imperial Coll London 11567 1.416 Univ Bristol 6681 1.367 Univ Coll London 13745 1.368 Univ Edinburgh 7142 1.329 VU Univ Amsterdam 6494 1.31
10 Erasmus Univ Rotterdam 6765 1.3111 Univ Zurich 7754 1.2912 Univ Amsterdam 8104 1.2913 King's Coll London 7249 1.2814 Leiden Univ 6355 1.2715 Univ Copenhagen 10157 1.2516 Aarhus Univ 7069 1.2417 Univ Manchester 10427 1.2318 Radboud Univ Nijmegen 6478 1.2319 Tech Univ München 6672 1.2220 Univ Groningen 6967 1.2221 Univ Nottingham 6839 1.2022 Karolinska Inst 8573 1.2023 Univ Southampton 6017 1.1924 Katholieke Univ Leuven 10956 1.1925 Univ Sheffield 6440 1.1926 LMUniv München 9585 1.1627 FAUniv Erlangen 5756 1.1628 Univ Paris-Sud 11 6323 1.1529 Univ Leeds 6627 1.1530 Univ Pierre & Marie Curie 9309 1.14
full <>frac
MNCS:
All:
Major changesfor the smaller universities
15
29
60!
For the smaller universitiesfractional counting is quite influential,
particularly for Delft!
EUR-100 / language: Engl / counting: fracP MNCS
1 Univ Göttingen 4776 2.042 EPF Lausanne 4790 1.693 Univ Cambridge 14046 1.534 ETH Zurich 8507 1.535 Utrecht Univ 9601 1.456 Univ Oxford 13919 1.447 Tech Univ Denmark 4287 1.438 Imperial Coll London 11547 1.379 Univ Bristol 6671 1.31
10 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 Amsterdam 6471 1.2716 LMUniv München 8261 1.2717 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 King's Coll London 7236 1.2323 Leiden Univ 6321 1.2324 FAUniv Erlangen 5198 1.2225 Univ Freiburg 4768 1.2226 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
EUR-100 / language: Engl / counting: fracP MNCS
1 Univ Göttingen 4776 2.042 EPF Lausanne 4790 1.693 Univ Cambridge 14046 1.534 ETH Zurich 8507 1.535 Utrecht Univ 9601 1.456 Univ Oxford 13919 1.447 Tech Univ Denmark 4287 1.438 Imperial Coll London 11547 1.379 Univ Bristol 6671 1.31
10 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 Amsterdam 6471 1.2716
Ludwig-Maximilians-Univ Münche 8261 1.27
17 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 King's Coll London 7236 1.2323 Leiden Univ 6321 1.2324 FAUniv Erlangen 5198 1.2225 Univ Freiburg 4768 1.2226 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
EUR-100 / language: Engl / counting: fracP
PPtop
10%
1 EPF Lausanne 4790 18.8%2 ETH Zurich 8507 17.6%3 Univ Cambridge 14046 16.7%4 Univ Oxford 13919 16.5%5 Tech Univ Denmark 4287 15.9%6 Imperial Coll London 11547 15.2%7 Univ Coll London 13723 14.8%8 Univ Edinburgh 7128 14.4%9 Wageningen Univ 4590 14.3%
10 Univ Zurich 7293 14.3%11 Univ Bristol 6671 14.2%12 Erasmus Univ Rotterdam 6735 14.2%13 VU Univ Amsterdam 6471 14.1%14 Univ Basel 4263 14.1%15 Univ Geneva 4971 13.9%16 LMUniv München 8261 13.8%17 Utrecht Univ 9601 13.6%18 Tech Univ München 6145 13.5%19 Leiden Univ 6321 13.3%20 Univ Amsterdam 8059 13.2%21 Univ Freiburg 4768 13.1%22 King's Coll London 7236 13.1%23 Delft Univ Technol 4531 12.9%24 Aarhus Univ 7060 12.9%25 Univ Paris-Sud 11 5866 12.9%26 FAUniv Erlangen 5198 12.8%27 Univ Copenhagen 10145 12.8%28 Karlsruhe Inst Technol 4362 12.7%29 Univ Pierre & Marie Curie 8416 12.7%30 Univ Glasgow 5262 12.6%62 !!
MNCS > p-top10%:Universities with rank position dominated by just 1 publication change considerably in position
500 largest universities worldwideP[2005-2009], C[2005-2010]
y = 0.14x - 0.04R2 = 0.97
0
0.05
0.1
0.15
0.2
0.25
0.3
0 0.5 1 1.5 2 2.5
MNCS-engl
pp(top10% )
EUR-50 / language: Engl / counting: fracP 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 Univ 8473 32.0%
10 Univ Oslo 6864 31.4%11 Univ Helsinki 7988 31.3%12 Univ Oxford 13919 31.1%13 Uppsala Univ 6928 30.9%14 Imperial Coll London 11547 30.5%15 Leiden Univ 6321 30.1%16 Univ Cambridge 14046 29.2%17 VU Univ Amsterdam 6471 28.8%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%
Collaboration and, more specifically, international collaboration:* no correlation with impact* weak correlation with MGCD (see Leiden Ranking website)
Correlation of p-int-collab with MGCDfor the 100 largest European universities
y = 0.01x0.56
R2 = 0.47
0.100
1.000
1000 10000MGCD
p-int-collab
Correlation of p-int-coll with MGCDfor Australia
y = 0,00x0,92
R2 = 0,94
0,10
1,00
1000,00 10000,00MGCD
p-int-coll
4. Evaluation tools related to the Leiden Ranking
Built on the basic data of the Leiden Ranking 2011:
Leiden Benchmark Analysis:
* all indicators of the ‘total’ university ranking are available for 30 main (NOWT) fields of science
* their trends in the past 10 years
* world rank by indicator and by main field
All 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 projects
Manchester, Leiden, Heidelberg, Rotterdam, Copenhagen, Zürich, Lisbon UNL, Amsterdam UvA, Amsterdam VU, SouthamptonGent, Antwerp, Brussels VUB,
UC London, Aarhus, Oslo
Output & impact compared to benchmark universities2002 - 2007
CHEMISTRY
ZurichUC London
Strasbourg I
Paris XI
Paris VIOxford
Milano
Lunds
LMU Munchen
Karolinska
KU Leuven
HelsinkiHeidelberg
Geneve
Freiburg
Edinburgh
Cambridge
Wageningen
Utrecht
Twente
TU DelftNijmegen
Leiden
GroningenEindhoven
VU Adam
UvA
0
0.5
1
1.5
2
0 500 1000 1500 2000 2500TOTAL PUBLI CATI ONS
CPP/ FCSm
Output and impact 2002 - 2007
SOCIAL SCIENCES
Zurich
UC London
Paris XI
Paris VI
Oxford
Milano
Lund
LMU Munchen
Karolinska
KU Leuven
Helsinki
Heidelberg
Geneve
Freiburg
Edinburgh
Cambridge
Wageningen
Utrecht
TwenteTilburg
TU Delft
Nijmegen
Maastricht
Leiden
Groningen
ErasmusEindhoven VU Adam
UvA
0
0,2
0,4
0,6
0,8
1
1,2
1,4
0 200 400 600 800 1000 1200 1400TOTAL PUBLICATIONS
CPP/ FCSm
FIGURE VII.12:RESEARCH AND IMPACT PROFILE COMPARISON CHART
2001 - 2008
0.91
1.15
0.83
1.52
1.45
1.14
1.08
1.41
1.20
1.44
1.42
1.15
1.27
1.25
1.55
0.92
1.37
1.19
1.17
4.70
1.11
1.17
1.10
1.20
1.25
1.20
1.62
0.84
1.06
1.01
1.95
1.10
0.88
1.34
2.00
1.01
1.20
1.13
1.25
2.12
1.17
0.98
1.31
1.98
1.33
1.25
1.00
1.46
2.04
2.44
0.98
1.04
0.70
1.25
1.12
1.10
0.97
0.87
0.94
1.17
7% 5% 3% 1% 1% 3% 5% 7%
ONCOLOGY
BIOCHEM&MOL BIOL
IMMUNOLOGY
CARD&CARDIOV SYS
PUBL ENV OCC HLT
ASTRON&ASTROPH
NEUROSCIENCES
PSYCHIATRY
HEMATOLOGY
CELL BIOLOGY
MEDICINE,GEN&INT
CLIN NEUROLOGY
ENDOCRIN&METABOL
PERIPHL VASC DIS
CHEM,PHYSICAL
SURGERY
GEOSC,MULTIDISC
ECOLOGY
DENT,ORAL SURG&M
PEDIATRICS
GENETICS&HEREDIT
OBSTETRICS&GYNEC
GASTROENTEROLOGY
PHYSICS,COND MAT
PHARMACOL&PHARMA
PHYSICS,MULTIDIS
NUTRITION&DIET
RAD,NUCL MED IM
PATHOLOGY
MEDICINE,RES&EXP
Percentage of Total Publication Output
IMPACT: LOW AVERAGE HIGH
UNIV OSLO LUNDS UNIV
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 1.91CLINICAL MEDICINE RHEUMATOLOGY 349.0 1.90CLINICAL MEDICINE CARDIAC & CARDIOVASC SYSTEMS 488.5 1.84BIOL SCI: HUMANS MEDICINE, RESEARCH & EXPER 100.0 1.81CLINICAL MEDICINE SURGERY 207.9 1.73CLINICAL MEDICINE UROLOGY & NEPHROLOGY 158.0 1.67MOLECULAR BIOL & BIOCHEM BIOTECH & APPLIED MICROBIOL 106.8 1.66SOCIAL SC RELATED TO MED PUBLIC, ENVIRONM & OCC HEALTH 111.6 1.65PHYSICS AND ASTRONOMY ASTRONOMY & ASTROPHYSICS 814.5 1.65BIOL SCI: HUMANS MICROBIOLOGY 179.3 1.58CLINICAL MEDICINE GENETICS & HEREDITY 394.9 1.57CLINICAL MEDICINE PERIPHERAL VASCULAR DISEASE 268.0 1.51
Leiden average MNCS = 1.45
It appears that the medical fields benefit more and the engineering fields less from the full counting
This will be very influential in the relative position of the engineering fields within a university, particularly in performance analyses
Shanghai and THES?? Use the Leiden Ranking from now on!
What !!??
Thank you for your attention
more information: www.cwts.leidenuniv.nl