Citation networks

Detecting communities in large networks is an important challenge in social network analysis, and providing an algorithm with optimal accuracy and efficiency for extracting communities is very important. There are different approaches to identify communities in social networks, including methods based on classical clustering, algorithms based on criteria of similarity in features, methods based on finding subgraphs with a lot of internal communication, as well as a label propagation approach. In the label propagation approach, first, the most important vertices of the network are determined based on a series of importance and centrality criteria, and different community labels are assigned to them. Then the label of each of these vertices is propagated to the neighboring vertices and around them. The aim of this research is to improve a community detection algorithm called LBLD. This algorithm first determines five percent of the most important network vertices based on a similarity criterion. Then, with a balanced approach, communities are developed both from the center and from the borders, and finally a phase of integration is implemented so that small communities are combined with each other and form desirable communities. Our proposed idea uses a measure inspired by the concept of h-index to improve the accuracy of community detection. In such a way that subgraphs are identified as communities that have at least p percent of vertices with at least degree k. The accuracy and efficiency of the proposed solution have been evaluated by applying it to known data sets in this field and it shows a significant improvement compared to existing similar methods

We present CiteWiz, an extensible framework for visualization of scientific citation networks. The system is based on a taxonomy of citation database usage for researchers, and provides a timeline visualization for overviews and an influence visualization for detailed views. The timeline displays the general chronology and importance of authors and articles in a citation database, whereas the influence visualization is implemented using the Growing Polygons technique, suitably modified to the context of browsing citation data. Using the latter technique, hierarchies of articles with potentially very long citation chains can be graphically represented. The visualization is augmented with mechanisms for parent–child visualization and suitable interaction techniques for interacting with the view hierarchy and the individual articles in the dataset. We also provide an interactive concept map for keywords and co-authorship using a basic force-directed graph layout scheme. A formal user s...

While many bibliometric techniques have been employed to represent the structure of academic research communities over the years, much of this work has been conducted on scientific fields as opposed to those in the humanities. Here we use graphing techniques to present two networks that allow us to explore the structure of a subset of the philosophy community by mapping the citations between philosophical texts on the topic of ontology (the study of what exists). We find a citation gap between philosophers studying material and abstract objects, and between analytic and continental ontologists, but other predictions were not confirmed by this method. We conclude by considering several additional methods for further exploring both the structure of philosophy and other disciplines in the humanities.

Complex systems play an essential role in our daily lives. These systems consist of many connected components that interact with each other. Consider, for example, society with billions of collaborating individuals, the stock market with numerous buyers and sellers that trade equities, or communication infrastructures with billions of phones, computers and satellites.

In this paper, we present VisualBib, a Web application, which allows users to create, visualize, modify, explore, and share bibliographies and the related citation networks, using innovative diagrams, called narrative views. The metadata are retrieved in real-time from four existing bibliographic indexes, Scopus, OpenCitations, and CrossRef/Orcid. Bibliographies and views are formally described and modelled using zz-structures, a semantic, nothierarchical data model. VisualBib has been evaluated through two evaluation studies, one focused on the quantitative side and another on the qualitative side. Taking into account both studies, they evaluate the tool regarding the effectiveness performing tasks, usability, graphic layout and other questions specific to the VisualBib features. The evaluation throws positive significant results in all areas when compared to Scopus searching features.

This thesis explores possible visual approaches for the representation of semantic structures, such as zz-structures. Some holistic visual representations of complex domains have been investigated through the proposal of new views - the so-called zz-views - that allow both to make visible the interconnections between elements and to support a contextual and multilevel exploration of knowledge. The potential of this approach has been examined in the context of two case studies that have led to the creation of two Web applications. The \ufb01rst domain of study regarded the visual representation, analysis and management of scienti\ufb01c bibliographies. In this context, we modeled a Web application, we called VisualBib, to support researchers in building, re\ufb01ning, analyzing and sharing bibliographies. We adopted a multi-faceted approach integrating features that are typical of three di\ufb00erent classes of tools: bibliography visual analysis systems, bibliographic citation index...

In this paper, we present VisualBib, a Web application, which allows users to create, visualize, modify, explore, and share bibliographies and the related citation networks, using innovative diagrams, called narrative views. The metadata are retrieved in real-time from four existing bibliographic indexes, Scopus, OpenCitations, and CrossRef/Orcid. Bibliographies and views are formally described and modelled using zz-structures, a semantic, nothierarchical data model. VisualBib has been evaluated through two evaluation studies, one focused on the quantitative side and another on the qualitative side. Taking into account both studies, they evaluate the tool regarding the effectiveness performing tasks, usability, graphic layout and other questions specific to the VisualBib features. The evaluation throws positive significant results in all areas when compared to Scopus searching features.

No âmbito da comunicação científica, pesquisadores vêm utilizando a metodologia de Análise de Redes Sociais (ARS) a fim de investigar como ocorre o relacionamento entre pesquisadores/instituições de ensino e pesquisa, organizações e documentos. A pesquisa presente neste artigo se insere nesse contexto: o objetivo foi mapear uma rede de citação dos autores que publicaram trabalhos sobre Competência em Informação (CI) na base de dados Scopus no ano de 2012. Foi utilizada a ARS em conjunto com aspectos técnicos da Análise de Citação para atingir ao objetivo proposto. A rede analisada apresentou um grau de densidade muito baixo, ou seja, houve um baixo índice de citação entre os autores; diferentemente das autocitações, que foram observadas em um número bastante alto. Foi possível constatar que determinados autores exercem grande importância por serem bastante citados pelos colegas. No caso da rede em questão, Christine Bruce foi identificada como a mais citada pelos colegas. Novas pesq...

Interdisciplinary teams are assembled in scientific research and are aimed at solving complex problems. Given their increasing importance, it is not surprising that considerable attention has been focused on processes of collaboration in interdisciplinary teams. Despite such efforts, we know less about the factors affecting the assembly of such teams in the first place. In this paper, we investigate the structure and the success of interdisciplinary scientific research teams. We examine the assembly factors using a sample of 1,103 grant proposals submitted to two National Science Foundation interdisciplinary initiatives during a 3-year period, including both awarded and non-awarded proposals. The results indicate that individuals' likelihood of collaboration on a proposal is higher among those with longer tenure, lower institutional tier, lower H-index, and with higher levels of prior co-authorship and citation relationships. However, successful proposals have a little bit different relational patterns: individuals' likelihood of collaboration is higher among those with lower institutional tier, lower H-index, (female) gender, higher levels of prior co-authorship, but with lower levels of prior citation relationships.

Do case citations reflect the "real" importance of individual judgments for the legal system concerned? This question has long been puzzling empirical legal scholars. Existing research typically studies case citation networks as a whole applying traditional network metrics stemming from graph theory. Those approaches are able to detect globally important cases, but since they do not take time explicitly into account, they cannot provide a comprehensive account of the dynamics behind the network structure and its evolution. In this paper we provide such a description, using two node importance metrics that take time into account to study important cases in the Court of Justice of the European Union over time. We then compare cases deemed as important by the metrics, with a set of 50 cases selected by the Court as the most important (landmark) cases. Our contribution is twofold. First, with regard to network science, we show that structural and time-related properties are complementary, and necessary to obtain a complete and nuanced picture of the citation network. Second, with regard to the case law of the Court, this study provides empirical evidence clarifying the motivation of the Court when selecting the landmark cases, revealing the importance of symbolic and historical cases in the selection. In addition, the temporal analysis sheds new light on the network properties specific to the landmark cases that distinguishes them from the rest of the cases. We validate our results by providing legal interpretations that sustain the highlights provided by the proposed network analysis.

How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature.

Even as we advance the frontiers of physics knowledge, our understanding of how this knowledge evolves remains at the descriptive levels of Popper and Kuhn. Using the American Physical Society (APS) publications data sets, we ask in this paper how new knowledge is built upon old knowledge. We do so by constructing year-to-year bibliographic coupling networks, and identify in them validated communities that represent different research fields. We then visualize their evolutionary relationships in the form of alluvial diagrams, and show how they remain intact through APS journal splits. Quantitatively, we see that most fields undergo weak Popperian mixing, and it is rare for a field to remain isolated/undergo strong mixing. The sizes of fields obey a simple linear growth with recombination. We can also reliably predict the merging between two fields, but not for the considerably more complex splitting. Finally, we report a case study of two fields that underwent repeated merging and splitting around 1995, and how these Kuhnian events are correlated with breakthroughs on Bose-Einstein condensation (BEC), quantum teleportation, and slow light. This impact showed up quantitatively in the citations of the BEC field as a larger proportion of references from during and shortly after these events.

Risk plays a fundamental role in scientific discoveries, and thus it is critical that the level of risk can be systematically quantified. We propose a novel approach to measuring risk entailed in a particular mode of discovery process – knowledge recombination. The recombination of extant knowledge serves as an important route to generate new knowledge, but attempts of recombination often fail. Drawing on machine learning and natural language processing techniques, our approach converts knowledge elements in the text format into high-dimensional vector expressions and computes the probability of failing to combine a pair of knowledge elements. Testing the calculated risk indicator on survey data, we confirm that our indicator is correlated with self-assessed risk. Further, as risk and novelty have been confounded in the literature, we examine and suggest the divergence of the bibliometric novelty and risk indicators. Finally, we demonstrate that our risk indicator is negatively associated with future citation impact, suggesting that risk-taking itself may not necessarily pay off. Our approach can assist decision making of scientists and relevant parties such as policymakers, funding bodies, and R&D managers.

Academic literature has been continuously growing at such a pace that it can be difficult to follow the progression of scientific achievements; hence, the need to dispose of quantitative knowledge support systems to analyze the literature of a subject. In this article we utilize network analysis tools to build a literature review of scientific documents published in the multidisciplinary field of Strategic Environment Assessment (SEA). The proposed approach helps researchers to build unbiased and comprehensive literature reviews. We collect information on 7662 SEA publications and build the SEA Bibliographic Network (SEABN) employing the basic idea that two publications are interconnected if one cites the other. We apply network analysis at macroscopic (network architecture), mesoscopic (sub graph) and microscopic levels (node) in order to i) verify what network structure characterizes the SEA literature, ii) identify the authors, disciplines and journals that are contributing to the international discussion on SEA, and iii) scrutinize the most cited and important publications in the field. Results show that the SEA is a multidisciplinary subject; the SEABN belongs to the class of real small world networks with a dominance of publications in Environmental studies over a total of 12 scientific sectors. Christopher Wood, Olivia Bina, Matthew Cashmore, and Andrew Jordan are found to be the leading authors while Environmental Impact Assessment Review is by far the scientific journal with the highest number of publications in SEA studies.

How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature.

The rate of scientific literature has been increased in the past few decades; new topics and information is added in the form of articles, papers, text documents, web logs, and patents. The growth of information at rapid rate caused a tremendous amount of additions in the current and past knowledge, during this process, new topics emerged, some topics split into many other sub-topics, on the other hand, many topics merge to formed single topic. The selection and search of a topic manually in such a huge amount of information have been found as an expensive and workforce-intensive task. For the emerging need of an automatic process to locate, organize, connect, and make associations among these sources the researchers have proposed different techniques that automatically extract components of the information presented in various formats and organize or structure them. The targeted data which is going to be processed for component extraction might be in the form of text, video or audio. The addition of different algorithms has structured information and grouped similar information into clusters and on the basis of their importance, weighted them. The organized, structured and weighted data is then compared with other structures to find similarity with the use of various algorithms. The semantic patterns can be found by employing visualization techniques that show similarity or relation between topics over time or related to a specific event. In this paper, we have proposed a model based on Cosine Similarity Algorithm for citation network which will answer the questions like, how to connect documents with the help of citation and content similarity and how to visualize and navigate through the document.

Interdisciplinary teams are assembled in scientific research and are aimed at solving complex problems. Given their increasing importance, it is not surprising that considerable attention has been focused on processes of collaboration in interdisciplinary teams. Despite such efforts, we know less about the factors affecting the assembly of such teams in the first place. In this paper, we investigate the structure and the success of interdisciplinary scientific research teams. We examine the assembly factors using a sample of 1,103 grant proposals submitted to two National Science Foundation interdisciplinary initiatives during a 3-year period, including both awarded and non-awarded proposals. The results indicate that individuals' likelihood of collaboration on a proposal is higher among those with longer tenure, lower institutional tier, lower H-index, and with higher levels of prior co-authorship and citation relationships. However, successful proposals have a little bit different relational patterns: individuals' likelihood of collaboration is higher among those with lower institutional tier, lower H-index, (female) gender, higher levels of prior co-authorship, but with lower levels of prior citation relationships.

To solve the most critical intellectual and social problems that confront us today, we need teams. A central challenge, spurred by new developments in cyberinfrastructure, is that the nature of teams and how they are assembled has changed radically. Today, many teams are ad hoc, agile, distributed, transient entities that emerge from a larger primordial network of relationships within virtual communities. Moreover, teams emerge from multidimensional networks, which include a variety of links that exist not only among individuals, but also ...

How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature.

Identification and tracking scientific changes is critical for scientific policy makers. This research proposed a model for identification and tracking changes in Data Quality research area. We used cumulative citation network in order to find research communities and their changes during the time. The proposed model can be applied in other scientific disciplines. It can also shows all types of scientific changes including birth, growth, merging and death. In order to verify the model in Data Quality area, we selected all papers that is published between 1970 and 2009 that covers more than 7000 papers. It is shown that Data Quality research area is studied in different disciplines. According to the results, there is 82 percent correlation between number of citations and the growth of Data Quality communities that shows the importance of citation for community survival and growth.

Do case citations reflect the "real" importance of individual judgments for the legal system concerned? This question has long been puzzling empirical legal scholars. Existing research typically studies case citation networks as a whole applying traditional network metrics stemming from graph theory. Those approaches are able to detect globally important cases, but since they do not take time explicitly into account, they cannot provide a comprehensive account of the dynamics behind the network structure and its evolution. In this paper we provide such a description, using two node importance metrics that take time into account to study important cases in the Court of Justice of the European Union over time. We then compare cases deemed as important by the metrics, with a set of 50 cases selected by the Court as the most important (landmark) cases. Our contribution is twofold. First, with regard to network science, we show that structural and time-related properties are complementary, and necessary to obtain a complete and nuanced picture of the citation network. Second, with regard to the case law of the Court, this study provides empirical evidence clarifying the motivation of the Court when selecting the landmark cases, revealing the importance of symbolic and historical cases in the selection. In addition, the temporal analysis sheds new light on the network properties specific to the landmark cases that distinguishes them from the rest of the cases. We validate our results by providing legal interpretations that sustain the highlights provided by the proposed network analysis.

To solve the most critical intellectual and social problems that confront us today, we need teams. A central challenge, spurred by new developments in cyberinfrastructure, is that the nature of teams and how they are assembled has changed radically. Today, many teams are ad hoc, agile, distributed, transient entities that emerge from a larger primordial network of relationships within virtual communities. Moreover, teams emerge from multidimensional networks, which include a variety of links that exist not only among individuals, but also ...

Originality has self-evident importance for science, but objectively measuring originality poses a formidable challenge. We conceptualise originality as the degree to which a scientific discovery provides subsequent studies with unique knowledge that is not available from previous studies. Accordingly, we operationalise a new measure of originality for individual scientific papers building on the network betweenness centrality concept. Specifically, we measure the originality of a paper based on the directed citation network between its references and the subsequent papers citing it. We demonstrate the validity of this measure using survey information. In particular, we find that the proposed measure is positively correlated with the self-assessed theoretical originality but not with the methodological originality. We also find that originality can be reliably measured with only a small number of subsequent citing papers, which lowers computational cost and contributes to practical ...

Any scientific activity requires awareness of previous related activities. Citation networks are the networks in which each document is compared as a link of a chain with its previous and next documents, and the documents with the highest number of citations are considered as the most effective ones in a domain. Most of the introduced methods use direct citations for valuing the documents. One of the challenges in ranking documents are indirect citations and determining effective features to compute the ranks. The presented method uses not only direct but also indirect citations for the valuing process. In this research, several measures for analyzing citation networks are introduced. The combination of these measures and using a data fusion method will improve the way by which documents are valued and ranked. In the presented method, an ordered weighted averaging fusion method is used to determine values based on the direct and indirect citations computed by some defined measures. Through some experiments, the presented method has been compared with some other fusion methods, which results indicate the effectiveness of our method.

The rate of scientific literature has been increased in the past few decades; new topics and information is added in the form of articles, papers, text documents, web logs, and patents. The growth of information at rapid rate caused a tremendous amount of additions in the current and past knowledge, during this process, new topics emerged, some topics split into many other sub-topics, on the other hand, many topics merge to formed single topic. The selection and search of a topic manually in such a huge amount of information have been found as an expensive and workforce-intensive task. For the emerging need of an automatic process to locate, organize, connect, and make associations among these sources the researchers have proposed different techniques that automatically extract components of the information presented in various formats and organize or structure them. The targeted data which is going to be processed for component extraction might be in the form of text, video or audio. The addition of different algorithms has structured information and grouped similar information into clusters and on the basis of their importance, weighted them. The organized, structured and weighted data is then compared with other structures to find similarity with the use of various algorithms. The semantic patterns can be found by employing visualization techniques that show similarity or relation between topics over time or related to a specific event. In this paper, we have proposed a model based on Cosine Similarity Algorithm for citation network which will answer the questions like, how to connect documents with the help of citation and content similarity and how to visualize and navigate through the document.

A partir de dados de jurisprudência do Conselho Nacional de Justiça foram extraídas 9.106 decisões entre 2005 e 2019 e verificados quais precedentes cada decisão judicial citava. A partir de técnicas de extração de conhecimento em base de dados e uso de redes complexas foi criada uma rede de citações entre precedentes judiciais com o objetivo de identificar os precedentes mais relevantes e similaridade entre os mesmos, através do uso de métricas de rede e de nós. Como resultado, foi demonstrada a validade do uso da métrica de centralidade de autovetor para caracterização da relevância de um precedente e a eficácia do uso da métrica de modularidade para identificação de precedentes similares.

To solve the most critical intellectual and social problems that confront us today, we need teams. A central challenge, spurred by new developments in cyberinfrastructure, is that the nature of teams and how they are assembled has changed radically. Today, many teams are ad hoc, agile, distributed, transient entities that emerge from a larger primordial network of relationships within virtual communities. Moreover, teams emerge from multidimensional networks, which include a variety of links that exist not only among individuals, but also ...

Collaborations among researchers and across disciplinary, organizational, and cultural boundaries are vital to address increasingly complex challenges and opportunities in science and society. In addition, unprecedented technological advances create new opportunities to capitalize on a broader range of expertise and information in scientific collaborations. Yet rapid increases in the demand for scientific collaborations have outpaced changes in the factors needed to support teams in science, such as institutional structures and policies, scientific culture, and funding opportunities. The Science of Team Science (SciTS) field arose with the goal of empirically addressing questions from funding agencies, administrators, and scientists regarding the value of team science (TS) and strategies for successfully leading, engaging in, facilitating, and supporting science teams. Closely related fields have rich histories studying teams, groups, organizations, and management and have built a b...

This Commentary describes recent research progress and professional developments in the study of scientific teamwork, an area of inquiry termed the “science of team science” (SciTS, pronounced “sahyts”). It proposes a systems perspective that incorporates a mixed-methods approach to SciTS that is commensurate with the conceptual, methodological, and translational complexities addressed within the SciTS field. The theoretically grounded and practically useful framework is intended to integrate existing and future lines of SciTS research to facilitate the field’s evolution as it addresses key challenges spanning macro, meso, and micro levels of analysis.

Collaborations among researchers and across disciplinary, organizational, and cultural boundaries are vital to address increasingly complex challenges and opportunities in science and society. In addition, unprecedented technological advances create new opportunities to capitalize on a broader range of expertise and information in scientific collaborations. Yet rapid increases in the demand for scientific collaborations have outpaced changes in the factors needed to support teams in science, such as institutional structures and policies, scientific culture, and funding opportunities. The Science of Team Science (SciTS) field arose with the goal of empirically addressing questions from funding agencies, administrators, and scientists regarding the value of team science (TS) and strategies for successfully leading, engaging in, facilitating, and supporting science teams. Closely related fields have rich histories studying teams, groups, organizations, and management and have built a b...

In today's world redundancy is the most vital problem faced in almost all domains. Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. The problem becomes more intense when it comes to "Research Articles". A method of identifying novelty at each sections of the article is highly required for determining the novel idea proposed in the research paper. Since research articles are semistructured, detecting novelty of information from them requires more accurate systems. Topic model provides a useful means to process them and provides a simple way to analyze them. This work compares the most predominantly used topic model-Latent Dirichlet Allocation with the hierarchical Pachinko Allocation Model. The results obtained are promising towards hierarchical Pachinko Allocation Model when used for document retrieval.

In today's world redundancy is the most vital problem faced in almost all domains. Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. The problem becomes more intense when it comes to "Research Articles". A method of identifying novelty at each sections of the article is highly required for determining the novel idea proposed in the research paper. Since research articles are semistructured, detecting novelty of information from them requires more accurate systems. Topic model provides a useful means to process them and provides a simple way to analyze them. This work compares the most predominantly used topic model-Latent Dirichlet Allocation with the hierarchical Pachinko Allocation Model. The results obtained are promising towards hierarchical Pachinko Allocation Model when used for document retrieval.

Although research collaboration has been studied extensively, we still lack understanding regarding the factors stimulating researchers to collaborate with different kinds of research partners including members of the same research center or group, researchers from the same organization, researchers from other academic and non-academic organizations as well as international partners. Here, we provide an explanation of the emergence of diverse collaborative ties. The theoretical framework used for understanding research collaboration couples scientific and technical human capital embodied in the individual with the social organization and cognitive characteristics of the research field. We analyze survey data collected from Slovenian scientists in four scientific disciplines: mathematics; physics; biotechnology; and sociology. The results show that while individual characteristics and resources are among the strongest predictors of collaboration, very different mechanisms underlie collaboration with different kinds of partners. International collaboration is particularly important for the researchers in small national science systems. Collaboration with colleagues from various domestic organizations presents a vehicle for resource mobilization. Within organizations collaboration reflects the elaborated division of labor in the laboratories and high level of competition between different research groups. These results hold practical implications for policymakers interested in promoting quality research.

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A partir de dados de jurisprudência do Conselho Nacional de Justiça foram extraídas 9.106 decisões entre 2005 e 2019 e verificados quais precedentes cada decisão judicial citava. A partir de técnicas de extração de conhecimento em base  de  dados  e  uso  de  redes  complexas  foi  criada  uma  rede  de  citações  entre  precedentes  judiciais  com  o  objetivo  de  identificar  os  precedentes  mais  relevantes  e  similaridade  entre  os  mesmos,  através  do  uso  de  métricas  de  rede  e  de  nós.  Como resultado, foi demonstrada a validade do uso da métrica de centralidade de autovetor para caracterização da relevância de um precedente e a eficácia do uso da métrica de modularidade para identificação de precedentes similares.


As from case law data of the National Council of Justice were extracted 9,106 decisions between 2005 and 2019 and verified which precedents each court decision cited. From database knowledge extraction techniques and the use of complex networks, a citation network between judicial precedents was created in order to identify the most relevant precedents and their similarity using network and nodes metrics. Results demonstrated the validity of using the eigenvector centrality metric to characterize precedent relevance and the effectiveness of using the modularity metric to identify similar precedents.

This study used the bibliometric method to examine the field of second language acquisition (SLA) between 1997 and 2018 to provide a systematic overview of the field and to discover major trends in SLA. Based on citation/co-citation information and keywords retrieved from Web of Science, this study performed three types of bibliometric analyses to identify the prominent scholarly documents, authors, research institutions, geographic regions, and research topics that have been highly influential in the field of SLA over the last two decades. The scientific network maps and the keyword analysis revealed a number of significant changes as well as new trends in the field.

In this research letter, I explain how the analysis of the citation network and of the co-citation network of a given topic and the use of the snowballing techniques can be useful to verify the eligibility of a paper for inclusion in the Living Systematic Review (LSR) of that topic.

In anticipation of the journal’s centenary in 2027 this paper provides a citation network analysis of all available citation and publication data of the Australasian Journal of Philosophy (1923-2017). A total of 2,353 academic articles containing 21,772 references, were collated and analysed. This includes 175 articles that contained author-submitted keywords, 415 publisher-tagged keywords and 519 articles that had abstracts. Results initially focused on finding the most published authors, most cited articles and most cited authors within the journal, followed by most discussed topics and emerging patterns using keywords and abstracts. The analysis then proceeded to apply social network analysis using Kumu©—a visualisation platform for mapping systems and relationships using large datasets. Analysis reveals topic clusters both unique to the journal, and inclusive of the journal’s history. Results from this analysis reaffirms the journal’s continuing focus on topics in traditional analytic philosophy such as morality, epistemology and knowledge, whilst also featuring topics associated with logic and paradox. This paper presents a new approach to analysing and understanding the historic and emerging topics of interest to the journal, and its readership. To our knowledge this has never previously been done for single philosophy journal. This is historically important given the journal’s forthcoming centenary. Results also offer wider implications for the future direction of the journal, and for research in philosophy generally. [Paper forthcoming in Global Intellectual History]

Even as we advance the frontiers of physics knowledge, our understanding of how this knowledge evolves remains at the descriptive levels of Popper and Kuhn. Using the Ameri-can Physical Society (APS) publications data sets, we ask in this paper how new knowledge is built upon old knowledge. We do so by constructing year-to-year bibliographic coupling networks, and identify in them validated communities that represent different research fields. We then visualize their evolutionary relationships in the form of alluvial diagrams, and show how they remain intact through APS journal splits. Quantitatively, we see that most fields undergo weak Popperian mixing, and it is rare for a field to remain isolated/undergo strong mixing. The sizes of fields obey a simple linear growth with recombination. We can also reliably predict the merging between two fields, but not for the considerably more complex splitting. Finally, we report a case study of two fields that underwent repeated merging and splitting around 1995, and how these Kuhnian events are correlated with breakthroughs on Bose-Einstein condensation (BEC), quantum teleportation, and slow light. This impact showed up quantitatively in the citations of the BEC field as a larger proportion of references from during and shortly after these events.

Finding research papers about particular topic of study is the most time consuming activity for many people including students, professors and researchers. People doing research have to search, read and analyze multiple research papers, e-books and other documents and then determine what they contain and discover knowledge from them. Huge available resources are in the form of unstructured texts format of long text pages which require a long time to process, search, read and analyze. Organizing research papers in their respective subjects or topics can facilitate the search process. We propose a new method to research paper organization and retrieval that is amenable to closely research papers and intertwined research topics. With our centroid and relationship based clustering approach, research papers are arranged and grouped within the most probable research topics or subjects. To determine topic membership, the proposed approach considers relationships such as common terms in pap...