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98
Network structure, self-organization, and the growth of international collaboration
- in science. Research Policy 34 1608--1618
, 2005
"... Using data from co-authorships at the international level in all fields of science in 1990 and 2000, and within six case studies at the sub-field level in 2000, different explanations for the growth of international collaboration in science and technology are explored. We find that few of the explan ..."
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Cited by 61 (6 self)
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Using data from co-authorships at the international level in all fields of science in 1990 and 2000, and within six case studies at the sub-field level in 2000, different explanations for the growth of international collaboration in science and technology are explored. We find that few of the explanations within the literature can be supported by a detailed review of the data. To enable further exploration of the role of recognition and rewards as ordering mechanisms within the system, we apply new tools emerging from network science. These enquiries shows that the growth of international co-authorships can be attributed to self-organizing phenomenon based on preferential attachment (searching for recognition and reward) within networks of co-authors. The co-authorship links can be considered as a complex network with sub-dynamics involving features of both competition and cooperation. The analysis suggests that the growth of international collaboration is more likely to emerge from dynamics at the sub-field level operating in all fields of science, albeit under institutional constraints. Implications for the management of global scientific collaborations are explored.
A Global Map of Science Based on the ISI Subject Categories
, 2007
"... The ISI subject categories classify journals included in the Science Citation Index (SCI). The aggregated journal-journal citation matrix contained in the Journal Citation Reports can be aggregated on the basis of these categories. This leads to an asymmetrical transaction matrix (citing versus cite ..."
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Cited by 45 (11 self)
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The ISI subject categories classify journals included in the Science Citation Index (SCI). The aggregated journal-journal citation matrix contained in the Journal Citation Reports can be aggregated on the basis of these categories. This leads to an asymmetrical transaction matrix (citing versus cited) which is much more densely populated than the underlying matrix at the journal level. Exploratory factor analysis leads us to opt for a fourteen-factor solution. This solution can easily be interpreted as the disciplinary structure of science. The nested maps of science (corresponding to 14 factors, 172 categories, and 6,164 journals) are brought online at
Betweenness Centrality” as an Indicator of the “Interdisciplinarity” of Scientific Journals
- Journal of the American Society for Information Science and Technology
, 2006
"... In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation ..."
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Cited by 45 (10 self)
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In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation Reports of the Science Citation Index and the Social Sciences Citation Index 2004, and then also in relation to local citation environments which can be considered as proxies of specialties and disciplines. Betweenness centrality is shown to be an indicator of the interdisciplinarity of journals, but only in local citation environments and after normalization because otherwise the influence of degree centrality (size) overshadows the betweenness-centrality measure. The indicator is applied to a variety of citation environments, including policy-relevant ones like biotechnology and nanotechnology. The values of the indicator remain sensitive to the delineations of the set because of the indicator’s local character. Maps showing 1 interdisciplinarity of journals in terms of betweenness centrality can be drawn using information about journal citation environments which is available online.
Content-based and Algorithmic Classifications of Journals: Perspectives on the
- Dynamics of Scientific Communication and Indexer Effects Journal of the American Society for Information Science and Technology, In print; DOI: 10.1002/asi.21086
, 2009
"... The aggregated journal-journal citation matrix—based on the Journal Citation Reports (JCR) of the Science Citation Index—can be decomposed by indexers and/or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two c ..."
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Cited by 38 (23 self)
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The aggregated journal-journal citation matrix—based on the Journal Citation Reports (JCR) of the Science Citation Index—can be decomposed by indexers and/or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two content-based classifications of journals: the ISI Subject Categories and the field/subfield classification of Glänzel & Schubert (2003). The content-based schemes allow for the attribution of more than a single category to a journal, whereas the algorithms maximize the ratio of within-category citations over between-category citations in the aggregated category-category citation matrix. By adding categories, indexers generate between-category citations, which may enrich the database, for example, in the case of inter-disciplinary developments. The consequent indexer effects are significant in sparse areas of the matrix more than in denser ones. Algorithmic decompositions, on the other hand, are more heavily skewed towards a relatively small number of categories, while this is deliberately counter-acted upon in the case of content-based classifications. Because of the indexer effects, science policy studies and the sociology of science should be careful when using content-based classifications, which are made for bibliographic disclosure, and not for the purpose of analyzing latent structures in scientific communications. Despite the large differences among them, the four classification schemes enable us to generate surprisingly similar maps of science at the global level. Erroneous classifications are cancelled as noise at the aggregate level, but may disturb the evaluation locally.
Dynamic animations of journal maps: Indicators of structural change and interdisciplinary developments.
- Journal of the American Society for Information Science and Technology
, 2008
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Visualizing Evolving Networks: Minimum Spanning Trees versus Pathfinder Networks
- IN IEEE SYMPOSIUM ON INFORMATION VISUALIZATION
, 2003
"... Network evolution is a ubiquitous phenomenon in a wide variety of complex systems. There is an increasing interest in statistically modeling the evolution of complex networks such as small-world networks and scale-free networks. In this article, we address a practical issue concerning the visualizat ..."
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Cited by 36 (4 self)
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Network evolution is a ubiquitous phenomenon in a wide variety of complex systems. There is an increasing interest in statistically modeling the evolution of complex networks such as small-world networks and scale-free networks. In this article, we address a practical issue concerning the visualization of network evolution. We compare the visualizations of co-citation networks of scientific publications derived by two widely known link reduction algorithms, namely minimum spanning trees (MSTs) and Pathfinder networks (PFNETs). Our primarily goal is to identify the strengths and weaknesses of the two methods in fulfilling the need for visualizing evolving networks. Two criteria are derived for assessing visualizations of evolving networks in terms of topological properties and dynamical properties. We examine the animated visualization models of the evolution of botulinum toxin research in terms of its co-citation structure across a 58-year span (1945-2002). The results suggest that although high-degree nodes dominate the structure of MST models, such structures can be inadequate in depicting the essence of how the network evolves because MST removes potentially significant links from high-order shortest paths. In contrast, PFNET models clearly demonstrate their superiority in maintaining the cohesiveness of some of the most pivotal paths, which in turn make the growth animation more predictable and interpretable. We suggest that the design of visualization and modeling tools for network evolution should take the cohesiveness of critical paths into account.
Caveats for the Use of Citation Indicators in Research and Journal Evaluations
"... Ageing of publications, percentage of self-citations, and impact vary from journal to journal within fields of science. The assumption that citation and publication practices are homogenous within specialties and fields of science is invalid. Furthermore, the delineation of fields and among specialt ..."
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Cited by 35 (14 self)
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Ageing of publications, percentage of self-citations, and impact vary from journal to journal within fields of science. The assumption that citation and publication practices are homogenous within specialties and fields of science is invalid. Furthermore, the delineation of fields and among specialties is fuzzy. Institutional units of analysis and persons may move between fields or span different specialties. The match between the citation index and institutional profiles varies among institutional units and nations. The respective matches may heavily affect the representation of the units. Non-ISI journals are increasingly cornered into “transdisciplinary ” Mode-2 functions with the exception of specialist journals publishing in languages other than English. An “externally cited impact factor ” can be calculated for these journals. The citation impact of non-ISI journals will be demonstrated using Science and Public Policy as the example.
Co-occurrence matrices and their applications in information science: Extending ACA to the web environment
- Journal of the American Society for Information Science and Technology
, 2006
"... Co-occurrence matrices, such as co-citation, co-word, and co-link matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of this data. The underlying problem, in our opinion, involved understanding the nature of v ..."
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Cited by 32 (9 self)
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Co-occurrence matrices, such as co-citation, co-word, and co-link matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of this data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This paper discusses the difference between a symmetrical co-citation matrix and an asymmetrical citation matrix as well as the appropriate statistical techniques that can be applied to each of these matrices, respectively. Similarity measures (like the Pearson correlation coefficient or the cosine) should not be applied to the symmetrical co-citation matrix, but can be applied to the asymmetrical citation matrix to derive the proximity matrix. The argument is illustrated with examples. The study then extends the application of co-occurrence matrices to the Web environment where the nature of the available data and thus data collection methods are different from those of traditional databases such as the Science Citation Index. A set of data collected with the Google Scholar search engine is analyzed using both the traditional methods of multivariate analysis and the new visualization software Pajek that is based on social network analysis and graph theory.
Action Recognition from One Example
, 2009
"... We present a novel action recognition method based on space-time locally adaptive regression kernels and the matrix cosine similarity measure. The proposed method uses a single example of an action to find similar matches. It does not require prior knowledge about actions; foreground/background segm ..."
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Cited by 29 (1 self)
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We present a novel action recognition method based on space-time locally adaptive regression kernels and the matrix cosine similarity measure. The proposed method uses a single example of an action to find similar matches. It does not require prior knowledge about actions; foreground/background segmentation, or any motion estimation or tracking. Our method is based on the computation of novel space-time descriptors from a query video, which measure the likeness of a voxel to its surroundings. Salient features are extracted from said descriptors and compared against analogous features from the target video. This comparison is done using a matrix generalization of the cosine similarity measure. The algorithm yields a scalar resemblance volume, with each voxel indicating the likelihood of similarity between the query video and all cubes in the target video. Using nonparametric significance tests and non-maxima suppression, we detect the presence and location of actions similar to the query video. High performance is demonstrated on challenging sets of action data containing fast motions, varied contexts, and even when multiple complex actions occur simultaneously within the field of view. Further experiments on the Weizmann and KTH datasets demonstrate state-of-the-art performance in action categorization, despite the use of only a single example.
Training-free, generic object detection using locally adaptive regression kernels
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2007
"... We present a generic detection/localization algorithm capable of searching for a visual object of interest without training. The proposed method operates using a single example of an object of interest to find similar matches; does not require prior knowledge (learning) about objects being sought; a ..."
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Cited by 27 (12 self)
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We present a generic detection/localization algorithm capable of searching for a visual object of interest without training. The proposed method operates using a single example of an object of interest to find similar matches; does not require prior knowledge (learning) about objects being sought; and does not require any pre-processing step or segmentation of a target image. Our method is based on the computation of local regression kernels as descriptors from a query, which measure the likeness of a pixel to its surroundings. Salient features are extracted from said descriptors and compared against analogous features from the target image. This comparison is done using a matrix generalization of the cosine similarity measure. We illustrate optimality properties of the algorithm using a naive-Bayes framework. The algorithm yields a scalar resemblance map, indicating the likelihood of similarity between the query and all patches in the target image. By employing nonparametric significance tests and non-maxima suppression, we detect the presence and location of objects similar to the given query. The approach is extended to account for large variations in scale and rotation. High performance is demonstrated on several challenging datasets, indicating successful detection of objects in diverse contexts and under different imaging conditions.
