Citation networks

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.

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.

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]

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.

Community detection helps us simplify the complex configuration of networks, but communities are reliable only if they are statistically significant. To detect statistically significant communities, a common approach is to resample the original network and analyze the communities. But resampling assumes independence between samples, while the components of a network are inherently dependent. Therefore, we must understand how breaking dependencies between resampled components affects the results of the significance analysis. Here we use scientific communication as a model system to analyze this effect. Our dataset includes citations among articles published in journals in the years 1984-2010. We compare parametric resampling of citations with non-parametric article resampling. While citation resampling breaks link dependencies, article resampling maintains such dependencies. We find that citation resampling underestimates the variance of link weights. Moreover, this underestimation explains most of the differences in the significance analysis of ranking and clustering. Therefore, when only link weights are available and article resampling is not an option, we suggest a simple parametric resampling scheme that generates link-weight variances close to the link-weight variances of article resampling. Nevertheless, when we highlight and summarize important structural changes in science, the more dependencies we can maintain in the resampling scheme, the earlier we can predict structural change.

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.

We present a study of the trends in the consumer electronics domain using Complex Social Network Analysis (SNA) of citation data retrieved from the Thomson Reuters Web of Knowledge. Our findings include the identification of the most influential papers and journals in the domain. In addition, our analysis provides proof of the key role of IEEE Transactions on Consumer Electronics in the domain of “Consumer Electronics” in terms of both Centrality as well as frequency of articles.

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.

Predicting future high impact academic papers is of benefit to a range of stakeholders, including governments, universities, academics, and investors. Being able to predict ‘the next big thing’ allows the allocation of resources to fields where these rapid developments are occurring. This paper develops a new method for predicting a paper’s future impact using features of the paper’s neighbourhood in the citation network, including measures of interdisciplinarity. Predictors of high impact papers include high early citation counts of the paper, high citation counts by the paper, citations of and by highly cited papers, and interdisciplinary citations of the paper and of papers that cite it. The Scopus database, consisting of over 24 million publication records from 1996-2010 across a wide range of disciplines, is used to motivate and evaluate the methods presented.

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 quick script to create a cross-citation network for a set of texts. Useful to explore citation and knowledge dissemination patterns. GNU Public License v3.0 (see GitHub repository or DOI for full license information). doi.org/10.5281/zenodo.1422870.

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.

В мировой научной сети Academia.edu, где зарегистрировано 51,328,813 Academics,мы стали обладателями последовательно шести виртуальных кубков, находясь в численаиболее читаемых учёных:
1. ТОР 5%.
2. ТОР 4%.
3. ТОР 3%.
4. ТОР 2%.
5. ТОР 1%.
6. ТОР 0,5%.

Community detection helps us simplify the complex configuration of networks, but communities are reliable only if they are statistically significant. To detect statistically significant communities, a common approach is to resample the original network and analyze the communities. But resampling assumes independence between samples, while the components of a network are inherently dependent. Therefore, we must understand how breaking dependencies between resampled components affects the results of the significance analysis. Here we use scientific communication as a model system to analyze this effect. Our dataset includes citations among articles published in journals in the years 1984-2010. We compare parametric resampling of citations with non-parametric article resampling. While citation resampling breaks link dependencies, article resampling maintains such dependencies. We find that citation resampling underestimates the variance of link weights. Moreover, this underestimation explains most of the differences in the significance analysis of ranking and clustering. Therefore, when only link weights are available and article resampling is not an option, we suggest a simple parametric resampling scheme that generates link-weight variances close to the link-weight variances of article resampling. Nevertheless, when we highlight and summarize important structural changes in science, the more dependencies we can maintain in the resampling scheme, the earlier we can predict structural change.

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.