@inproceedings{marco2004hedging, abstract = {Citations in scientific writing fulfil an important role in creating relationships among mutually relevant articles within a research field. These inter-article relationships reinforce the argumentation structure intrinsic to all scientific writing. Therefore, determining the nature of the exact relationship between a citing and cited paper requires an understanding of the rhetorical relations within the argumentative context in which a citation is placed. To automatically determine these relations, we have suggested that various stylistic and rhetorical cues will be significant. One such cue that we are studying is the use of hedging to modify the affect of a scientific claim. We have previously shown that hedging occurs more frequently in citation contexts than in the text as a whole. With this information we conjecture that hedging is a significant aspect of the rhetorical structure of citation contexts and that the pragmatics of hedges may help in determining the rhetorical purpose of citations.}, author = {Marco, Chrysanne Di and Mercer, Robert E.}, booktitle = {Proc. AAAI Spring Symposium}, interhash = {7c4532214d65b2ef202fd2a7d7311901}, intrahash = {90a6c053558d98c9bfdbb6b4d6d8d725}, organization = {AAAI}, title = {Hedging in Scientific Articles as a Means of Classifying Citations}, url = {http://www.aaai.org/Library/Symposia/Spring/2004/ss04-07-009.php}, year = 2004 } @article{nanba2000classification, abstract = {We are investigating automatic generation of a review (or survey) article in a specific subject domain. In a research paper, there are passages where the author describes the essence of a cited paper and the differences between the current paper and the cited paper (we call them citing areas). These passages can be considered as a kind of summary of the cited paper from the current author's viewpoint. We can know the state of the art in a specific subject domain from the collection of citing areas. FUrther, if these citing areas are properly classified and organized, they can act 8.', a kind of a review article. In our previous research, we proposed the automatic extraction of citing areas. Then, with the information in the citing areas, we automatically identified the types of citation relationships that indicate the reasons for citation (we call them citation types). Citation types offer a useful clue for organizing citing areas. In addition, to support writing a review article, it is necessary to take account of the contents of the papers together with the citation links and citation types. In this paper, we propose several methods for classifying papers automatically. We found that our proposed methods BCCT-C, the bibliographic coupling considering only type C citations, which pointed out the problems or gaps in related works, are more effective than others. We also implemented a prototype system to support writing a review article, which is based on our proposed method.}, author = {Nanba, H. and Kando, N. and Okumura, M.}, interhash = {a8fbc36d3ee8de28f65ef2486bb18cd2}, intrahash = {7a99ee2d1444ae569beb7bee04137e4b}, journal = {11th ASIS SIG/CR Classification Research Workshop}, misc = {10.7152/acro.v11i1.12774}, pages = {117--134}, title = {Classification of research papers using citation links and citation types: Towards automatic review article generation}, url = {http://journals.lib.washington.edu/index.php/acro/article/download/12774/11255}, year = 2000 } @article{liu2012fulltext, author = {Liu, Xiaozhong and Zhang, Jinsong and Guo, Chun}, interhash = {011df26355ad51a88947017fd2791a98}, intrahash = {f9c6133bf4503003822f99860f864698}, journal = {Journal of the American Society for Information Science and Technology}, title = {Full-Text Citation Analysis: A New Method to Enhance Scholarly Network}, url = {http://discern.uits.iu.edu:8790/publication/Full%20text%20citation.pdf}, year = 2012 } @inproceedings{yan2012better, abstract = {Usually scientists breed research ideas inspired by previous publications, but they are unlikely to follow all publications in the unbounded literature collection. The volume of literature keeps on expanding extremely fast, whilst not all papers contribute equal impact to the academic society. Being aware of potentially influential literature would put one in an advanced position in choosing important research references. Hence, estimation of potential influence is of great significance. We study a challenging problem of identifying potentially influential literature. We examine a set of hypotheses on what are the fundamental characteristics for highly cited papers and find some interesting patterns. Based on these observations, we learn to identify potentially influential literature via Future Influence Prediction (FIP), which aims to estimate the future influence of literature. The system takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period. We consider several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R2). Experimental results on a real-large data set show a mean average predictive performance of 83.6% measured in R^2. We apply the learned model to the application of bibliography recommendation and obtain prominent performance improvement in terms of Mean Average Precision (MAP).}, acmid = {2232831}, address = {New York, NY, USA}, author = {Yan, Rui and Huang, Congrui and Tang, Jie and Zhang, Yan and Li, Xiaoming}, booktitle = {Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries}, doi = {10.1145/2232817.2232831}, interhash = {85d10c6d37bcbfa057c51acc325a8116}, intrahash = {9269d2dd9bf4bc8c0e7c668011fcfc1b}, isbn = {978-1-4503-1154-0}, location = {Washington, DC, USA}, numpages = {10}, pages = {51--60}, publisher = {ACM}, series = {JCDL '12}, title = {To better stand on the shoulder of giants}, url = {http://doi.acm.org/10.1145/2232817.2232831}, year = 2012 } @inproceedings{yan2011citation, abstract = {In most of the cases, scientists depend on previous literature which is relevant to their research fields for developing new ideas. However, it is not wise, nor possible, to track all existed publications because the volume of literature collection grows extremely fast. Therefore, researchers generally follow, or cite merely a small proportion of publications which they are interested in. For such a large collection, it is rather interesting to forecast which kind of literature is more likely to attract scientists' response. In this paper, we use the citations as a measurement for the popularity among researchers and study the interesting problem of Citation Count Prediction (CCP) to examine the characteristics for popularity. Estimation of possible popularity is of great significance and is quite challenging. We have utilized several features of fundamental characteristics for those papers that are highly cited and have predicted the popularity degree of each literature in the future. We have implemented a system which takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period. We consider several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R-square). Experimental results on a real-large data set show that the best predictive model achieves a mean average predictive performance of 0.740 measured in R-square, which significantly outperforms several alternative algorithms.}, acmid = {2063757}, address = {New York, NY, USA}, author = {Yan, Rui and Tang, Jie and Liu, Xiaobing and Shan, Dongdong and Li, Xiaoming}, booktitle = {Proceedings of the 20th ACM international conference on Information and knowledge management}, doi = {10.1145/2063576.2063757}, interhash = {71ec0933a36df3dd21f38285bdf9b1b0}, intrahash = {b0caabb6e17d9b790d3f13c897330aad}, isbn = {978-1-4503-0717-8}, location = {Glasgow, Scotland, UK}, numpages = {6}, pages = {1247--1252}, publisher = {ACM}, series = {CIKM '11}, title = {Citation count prediction: learning to estimate future citations for literature}, url = {http://doi.acm.org/10.1145/2063576.2063757}, year = 2011 } @article{boerner2012design, abstract = {Global maps of science can be used as a reference system to chart career trajectories, the location of emerging research frontiers, or the expertise profiles of institutes or nations. This paper details data preparation, analysis, and layout performed when designing and subsequently updating the UCSD map of science and classification system. The original classification and map use 7.2 million papers and their references from Elsevier’s Scopus (about 15,000 source titles, 2001–2005) and Thomson Reuters’ Web of Science (WoS) Science, Social Science, Arts & Humanities Citation Indexes (about 9,000 source titles, 2001–2004)–about 16,000 unique source titles. The updated map and classification adds six years (2005–2010) of WoS data and three years (2006–2008) from Scopus to the existing category structure–increasing the number of source titles to about 25,000. To our knowledge, this is the first time that a widely used map of science was updated. A comparison of the original 5-year and the new 10-year maps and classification system show (i) an increase in the total number of journals that can be mapped by 9,409 journals (social sciences had a 80% increase, humanities a 119% increase, medical (32%) and natural science (74%)), (ii) a simplification of the map by assigning all but five highly interdisciplinary journals to exactly one discipline, (iii) a more even distribution of journals over the 554 subdisciplines and 13 disciplines when calculating the coefficient of variation, and (iv) a better reflection of journal clusters when compared with paper-level citation data. When evaluating the map with a listing of desirable features for maps of science, the updated map is shown to have higher mapping accuracy, easier understandability as fewer journals are multiply classified, and higher usability for the generation of data overlays, among others.}, author = {Börner, Katy and Klavans, Richard and Patek, Michael and Zoss, Angela M. and Biberstine, Joseph R. and Light, Robert P. and Larivière, Vincent and Boyack, Kevin W.}, doi = {10.1371/journal.pone.0039464}, interhash = {c27eeafd6c2d77f7022ce10236e3dd47}, intrahash = {494fdcbea8cd30a1a04a06aa1696fce6}, journal = {PLoS ONE}, month = jul, number = 7, pages = {e39464}, publisher = {Public Library of Science}, title = {Design and Update of a Classification System: The UCSD Map of Science}, url = {http://dx.doi.org/10.1371%2Fjournal.pone.0039464}, volume = 7, year = 2012 } @article{larowe2009scholarly, abstract = {The Scholarly Database aims to serve researchers and practitioners interested in the analysis, modelling, and visualization of large-scale data sets. A specific focus of this database is to support macro-evolutionary studies of science and to communicate findings via knowledge-domain visualizations. Currently, the database provides access to about 18 million publications, patents, and grants. About 90% of the publications are available in full text. Except for some datasets with restricted access conditions, the data can be retrieved in raw or pre-processed formats using either a web-based or a relational database client. This paper motivates the need for the database from the perspective of bibliometric/scientometric research. It explains the database design, setup, etc., and reports the temporal, geographical, and topic coverage of data sets currently served via the database. Planned work and the potential for this database to become a global testbed for information science research are discussed at the end of the paper.}, author = {La Rowe, Gavin and Ambre, Sumeet and Burgoon, John and Ke, Weimao and Börner, Katy}, doi = {10.1007/s11192-009-0414-2}, interhash = {1819f263b0ea1b99ec15d0c22b38207e}, intrahash = {c24611ec1f2efbdcf7f5b26d49af320e}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 2, pages = {219--234}, publisher = {Springer Netherlands}, title = {The Scholarly Database and its utility for scientometrics research}, url = {http://dx.doi.org/10.1007/s11192-009-0414-2}, volume = 79, year = 2009 } @techreport{ritchie2009citation, abstract = {This thesis investigates taking words from around citations to scientific papers in order to create an enhanced document representation for improved information retrieval. This method parallels how anchor text is commonly used in Web retrieval. In previous work, words from citing documents have been used as an alternative representation of the cited document but no previous experiment has combined them with a full-text document representation and measured effectiveness in a large scale evaluation. The contributions of this thesis are twofold: firstly, we present a novel document representation, along with experiments to measure its effect on retrieval effectiveness, and, secondly, we document the construction of a new, realistic test collection of scientific research papers, with references (in the bibliography) and their associated citations (in the running text of the paper) automatically annotated. Our experiments show that the citation-enhanced document representation increases retrieval effectiveness across a range of standard retrieval models and evaluation measures. In Chapter 2, we give the background to our work, discussing the various areas from which we draw together ideas: information retrieval, particularly link structure analysis and anchor text indexing, and bibliometrics, in particular citation analysis. We show that there is a close relatedness of ideas between these areas but that these ideas have not been fully explored experimentally. Chapter 3 discusses the test collection paradigm for evaluation of information retrieval systems and describes how and why we built our test collection. In Chapter 4, we introduce the ACL Anthology, the archive of computational linguistics papers that our test collection is centred around. The archive contains the most prominent publications since the beginning of the field in the early 1960s, consisting of one journal plus conferences and workshops, resulting in over 10,000 papers. Chapter 5 describes how the PDF papers are prepared for our experiments, including identification of references and citations in the papers, once converted to plain text, and extraction of citation information to an XML database. Chapter 6 presents our experiments: we show that adding citation terms to the full-text of the papers improves retrieval effectiveness by up to 7.4%, that weighting citation terms higher relative to paper terms increases the improvement and that varying the context from which citation terms are taken has a significant effect on retrieval effectiveness. Our main hypothesis that citation terms enhance a full-text representation of scientific papers is thus proven. There are some limitations to these experiments. The relevance judgements in our test collection are incomplete but we have experimentally verified that the test collection is, nevertheless, a useful evaluation tool. Using the Lemur toolkit constrained the method that we used to weight citation terms; we would like to experiment with a more realistic implementation of term weighting. Our experiments with different citation contexts did not conclude an optimal citation context; we would like to extend the scope of our investigation. Now that our test collection exists, we can address these issues in our experiments and leave the door open for more extensive experimentation. }, address = {Cambridge, UK}, author = {Ritchie, Anna}, institution = {University of Cambridge}, interhash = {f086fdcd7eb1df44ef67b96f2e91996c}, intrahash = {aa4271a2a958fe2c1a65dbdd508d8de7}, issn = {1476-2986}, month = mar, number = 744, title = {Citation context analysis for information retrieval}, url = {https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-744.pdf}, year = 2009 } @inproceedings{aya2005citation, abstract = {Citation analysis has been used to study various aspects of scholarly communication. In general, these studies have not differentiated among the multiple reasons for citations. However, authors cite other works for a number of reasons including demonstrating knowledge of the field, establishing the placement of the citing work in the field, comparing and criticizing other works, and paying homage to seminal work by pioneers in the field. In this paper, we present a number of applications in which distinguishing among authors' motivations for citations might be useful and present a machine learning approach to automatically classifying citations according to these motivations. Our approach to citation classification makes use of the structure and the argumentative nature of the scientific papers. We present the results of experiments we ran on papers in the computer science field. The results are encouraging and give us hope that we can use our citation classifier in analyzing large corpora of scientific papers.}, author = {Aya, Selcuk and Lagoze, Carl and Joachims, Thorsten}, booktitle = {Proceedings of the International Conference on Knowledge Management}, chapter = 24, doi = {10.1142/9789812701527_0024}, eprint = {http://www.worldscientific.com/doi/pdf/10.1142/9789812701527_0024}, interhash = {f35b1f099571f3f134186ff407ee5fee}, intrahash = {d30bac9f744e0473499f1d15d55258b8}, month = oct, pages = {287--298}, publisher = {World Scientific Publishing}, title = {Citation Classification and its Applications}, url = {http://www.worldscientific.com/doi/abs/10.1142/9789812701527_0024}, year = 2005 } @article{sun2013social, abstract = {The birth and decline of disciplines are critical to science and society. How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.}, author = {Sun, Xiaoling and Kaur, Jasleen and Milojevic, Stasa and Flammini, Alessandro and Menczer, Filippo}, doi = {10.1038/srep01069}, interhash = {5cd31392e997555d78596f962044f84b}, intrahash = {721dcd5644cca27fd50d8e6ffd667056}, journal = {Scientific Reports}, month = jan, publisher = {Macmillan Publishers Limited}, title = {Social Dynamics of Science}, url = {http://dx.doi.org/10.1038/srep01069}, volume = 3, year = 2013 } @article{aguillo2009measuring, abstract = {Purpose – The purpose of this paper is to provide an alternative, although complementary, system for the evaluation of the scholarly activities of academic organizations, scholars and researchers, based on web indicators, in order to speed up the change of paradigm in scholarly communication towards a new fully electronic twenty-first century model. Design/methodology/approach – In order to achieve these goals, a new set of web indicators has been introduced, obtained mainly from data gathered from search engines, the new mediators of scholarly communication. Findings – It was found that three large groups of indicators are feasible to obtain and relevant for evaluation purposes: activity (web publication); impact (visibility) and usage (visits and visitors). As a proof of concept, a Ranking Web of Universities has been built with Webometrics data. There are two relevant findings: ranking results are similar to those obtained by other bibliometric-based rankings; and there is a concerning digital divide between North American and European universities, which appear in lower positions when compared with their USA and Canada counterparts. Research limitations/implications – Cybermetrics is still an emerging discipline, so new developments should be expected when more empirical data become available. Practical implications – The proposed approach suggests the publication of truly electronic journals, rather than digital versions of printed articles. Additional materials, such as raw data and multimedia files, should be included along with other relevant information arising from more informal activities. These repositories should be Open Access, available as part of the public web, indexed by the main commercial search engines. It is expected that these actions could generate larger web-based audiences, reduce the costs of publication and access and allow third parties to take advantage of the knowledge generated, without sacrificing peer review, which should be extended (pre- and post-) and expanded (closed and open). Originality/value – A full taxonomy of web indicators is introduced for describing and evaluating research activities, academic organizations and individual scholars and scientists. Previous attempts for building such classification were incomplete and did not take into account feasibility and efficiency.}, address = {Bingley}, author = {Aguillo, Isidro}, doi = {10.1108/073788309}, interhash = {116e889174766cd359f7e79eb1a36302}, intrahash = {9734b1f272204883b30dedaa8069fdad}, issn = {0737-8831}, journal = {Library Hi Tech}, number = 4, pages = {540--556}, publisher = {Emerald Group Publishing Limited}, title = {Measuring the institution's footprint in the web}, url = {http://www.emeraldinsight.com/journals.htm?articleid=1812469&show=abstract}, volume = 27, year = 2009 } @article{thelwall2012journal, abstract = {In theory, the web has the potential to provide information about the wider impact of academic research, beyond traditional scholarly impact. This is because the web can reflect non-scholarly uses of research, such as in online government documents, press coverage or public discussions. Nevertheless, there are practical problems with creating metrics for journals based on web data: principally that most such metrics should be easy for journal editors or publishers to manipulate. Nevertheless, two alternatives seem to have both promise and value: citations derived from digitised books and download counts for journals within specific delivery platforms.}, author = {Thelwall, Mike}, doi = {10.1007/s11192-012-0669-x}, interhash = {834707cf0663109f7811a14ae746be72}, intrahash = {284883bbaa636a0bab13fc54b903f363}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 2, pages = {429--441}, publisher = {Springer Netherlands}, title = {Journal impact evaluation: a webometric perspective}, url = {http://dx.doi.org/10.1007/s11192-012-0669-x}, volume = 92, year = 2012 } @article{birkholz2012scalable, abstract = {Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific socially informed model for predicting links from both network and social properties in large social networks. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters. }, author = {Birkholz, Julie M. and Bakhshi, Rena and Harige, Ravindra and van Steen, Maarten and Groenewegen, Peter}, interhash = {a8ef0aac2eab74fc8eb3f9d3dc8a32dd}, intrahash = {aefcc2aa922b048bec85d5070494ed81}, journal = {CoRR}, month = sep, title = {Scalable Analysis of Socially Informed Network Models: the data-aware mean-field approach }, url = {http://arxiv.org/abs/1209.6615}, volume = {abs/1209.6615}, year = 2012 } @article{ortega2009mapping, abstract = {A visual display of the most important universities in the world is the aim of this paper. It shows the topological characteristics and describes the web relationships among universities of different countries and continents. The first 1000 higher education institutions from the Ranking Web of World Universities were selected and their link relationships were obtained from Yahoo! Search. Network graphs and geographical maps were built from the search engine data. Social network analysis techniques were used to analyse and describe the structural properties of the whole of the network and its nodes. The results show that the world-class university network is constituted from national sub-networks that merge in a central core where the principal universities of each country pull their networks toward international link relationships. The United States dominates the world network, and within Europe the British and the German sub-networks stand out.}, author = {Ortega, Jose Luis and Aguillo, Isidro F.}, doi = {10.1016/j.ipm.2008.10.001}, interhash = {1c46addf1c5019aa75a11365a35da757}, intrahash = {f5960b8cb33d8b10b82abadd17b9a4e7}, issn = {0306-4573}, journal = {Information Processing & Management}, number = 2, pages = {272--279}, title = {Mapping world-class universities on the web}, url = {http://www.sciencedirect.com/science/article/pii/S0306457308001015}, volume = 45, year = 2009 } @article{ortega2008academic, abstract = {This paper shows maps of the web presence of the European Higher Education Area (EHEA) on the level of universities using hyperlinks and analyses the topology of the European academic network. Its purpose is to combine methods from Social Network Analysis (SNA) and cybermetric techniques in order to ask for tendencies of integration of the European universities visible in their web presence and the role of different universities in the process of the emergence of an European Research Area. We find as a main result that the European network is set up by the aggregation of well-defined national networks, whereby the German and British networks are dominant. The national networks are connected to each other through outstanding national universities in each country.}, affiliation = {CINDOC-CSIC Cybermetrics Lab Joaquín Costa, 22 28002 Madrid Spain}, author = {Ortega, Jose and Aguillo, Isidro and Cothey, Viv and Scharnhorst, Andrea}, doi = {10.1007/s11192-008-0218-9}, interhash = {9c9dcd171a50875d82f75f5a12d3c41f}, intrahash = {22102ef820bb00f432c842edf6e98a1f}, issn = {0138-9130}, journal = {Scientometrics}, keyword = {Computer Science}, number = 2, pages = {295--308}, publisher = {Akadémiai Kiadó}, title = {Maps of the academic web in the European Higher Education Area — an exploration of visual web indicators}, url = {http://dx.doi.org/10.1007/s11192-008-0218-9}, volume = 74, year = 2008 } @article{mccain2010journal, abstract = {This paper reports two interrelated citation-based studies of the intellectual structure of Evolutionary Developmental Biology (Evo-Devo). The core journals of Evo-Devo (Evolution & Development, Development, Genes & Evolution, and Journal of Experimental Zoology, pt. B) and its supporting/parental disciplines are identified and their strong citation links mapped based on data from Journal Citation Reports, 2005–2007. Evo-Devo cites into Developmental Biology in all three years and exchanges citations with Paleontology in 2007. There are no strong connections with either general or molecular Evolution journals. Persistent, visible research themes are visualized as citing-cited networks and subnetworks of articles extracted from the Web of Science for the core Evo-Devo journals and a larger set of articles citing one or more Evo-Devo journals. Most research themes in the core set are specific to a single journal. Few highly cited core journal articles are also visible in the broader set of networks and subnetworks, although some themes (e.g., arthropod body plans, chordate genes/gene expression) are visible in both data sets.}, author = {McCain, Katherine W.}, doi = {10.1016/j.joi.2009.11.004}, interhash = {300ce0bc90f6b1d5cc2d386cdddfb854}, intrahash = {5c2ec6a0e3de448accb765191ffda5aa}, issn = {1751-1577}, journal = {Journal of Informetrics}, number = 2, pages = {157--165}, title = {Core journal literatures and persistent research themes in an emerging interdisciplinary field: Exploring the literature of evolutionary developmental biology}, url = {http://www.sciencedirect.com/science/article/pii/S1751157709000820}, volume = 4, year = 2010 } @article{tian2008global, abstract = {A bibliometric analysis was applied in this work to evaluate global scientific production of geographic information system (GIS) papers from 1997 to 2006 in any journal of all the subject categories of the Science Citation Index compiled by Institute for Scientific Information (ISI), Philadelphia, USA. ‘GIS’ and ‘geographic information system’ were used as keywords to search parts of titles, abstracts, or keywords. The published output analysis showed that GIS research steadily increased over the past 10 years and the annual paper production in 2006 was about three times 1997s paper production. There are clear distinctions among author keywords used in publications from the five most productive countries (USA, UK, Canada, Germany and China) in GIS research. Bibliometric methods could quantitatively characterize the development of global scientific production in a specific research field. The analytical results eventually provide several key findings.}, author = {Tian, Yangge and Wen, Cheng and Hong, Song}, doi = {10.1016/j.joi.2007.10.001}, interhash = {5bb2b42b4832f8f84c11a1998981d5ba}, intrahash = {36c5d436fb45d8649b44923a430f2106}, issn = {1751-1577}, journal = {Journal of Informetrics}, number = 1, pages = {65--74}, title = {Global scientific production on GIS research by bibliometric analysis from 1997 to 2006}, url = {http://www.sciencedirect.com/science/article/pii/S1751157707000715}, volume = 2, year = 2008 } @article{pham2011development, abstract = {In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.}, address = {Wien}, affiliation = {Information Systems and Database Technology, RWTH Aachen University, Aachen, Ahornstr. 55, 52056 Aachen, Germany}, author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias}, doi = {10.1007/s13278-011-0024-x}, interhash = {193312234ed176aa8be9f35d4d1c4e72}, intrahash = {8ae08cacda75da80bfa5604cfce48449}, issn = {1869-5450}, journal = {Social Network Analysis and Mining}, keyword = {Computer Science}, number = 4, pages = {321--340}, publisher = {Springer}, title = {Development of computer science disciplines: a social network analysis approach}, url = {http://dx.doi.org/10.1007/s13278-011-0024-x}, volume = 1, year = 2011 } @article{collective2011measuring, author = {Collective, Cynical Geographers}, doi = {10.1111/j.1467-8330.2010.00873.x}, interhash = {313844c34e760e283a24049a8b5d390c}, intrahash = {311db90923f95bbc69d64978d06fb0f7}, issn = {1467-8330}, journal = {Antipode}, number = 2, pages = {190--194}, publisher = {Blackwell Publishing Ltd}, title = {Measuring Impact Beyond Academic Fame: An Alternative Social Impact Factor}, url = {http://dx.doi.org/10.1111/j.1467-8330.2010.00873.x}, volume = 43, year = 2011 } @article{falagas2008topten, abstract = {A considerable part of the scientific community is, at least to some degree, involved in the “impact factor game” Editors strive to increase their journals — impact factor (IF) in order to gain influence in the fields of basic and applied research and scientists seek to profit from the “added value” of publishing in top IF journals. In this article we point out the most common “tricks” of engineering and manipulating the IF undertaken by a portion of professionals of the scientific publishing industry. They attempt to increase the nominator or decrease the denominator of the IF equation by taking advantage of certain design flaws and disadvantages of the IF that permit a degree of artificial and arbitrary inflation. Some of these practices, if not scientifically unethical, are at least questionable and should be abandoned. Editors and publishers should strive for quality through fair and thoughtful selection of papers forwarded for peer review and editorial comments that enhance the quality and scientific accuracy of a manuscript.}, address = {Basel}, affiliation = {Alfa Institute of Biomedical Sciences (AIBS) 9 Neapoleos Street 151 23 Marousi, Athens Greece}, author = {Falagas, Matthew and Alexiou, Vangelis}, doi = {10.1007/s00005-008-0024-5}, interhash = {8350623ff88fab4e9405805d9f5663b9}, intrahash = {46e10496e8530bb408702d2cc58f73e8}, issn = {0004-069X}, issue = {4}, journal = {Archivum Immunologiae et Therapiae Experimentalis}, keyword = {Biomedical and Life Sciences}, pages = {223--226}, publisher = {Birkhäuser}, title = {The top-ten in journal impact factor manipulation}, url = {http://dx.doi.org/10.1007/s00005-008-0024-5}, volume = 56, year = 2008 }