Text and Graph Based Approach for Analyzing Patterns of Research Collaboration: An analysis of the TrueImpactDataset

Patterns of scientific collaboration and their effect on scientific production have been the subject of many studies. In this paper, we analyze the nature of ties between co-authors and study collaboration patterns in science from the perspective of semantic similarity of authors who wrote a paper together and the strength of ties between these authors (i.e. how frequently have they previously collaborated together). These two views of scientific collaboration are used to analyze publications in the TrueImpactDataset (Herrmannova et al., 2017) (Herrmannova et al., 2017), a new dataset containing two types of publications – publications regarded as seminal and publications regarded as literature reviews by field experts. We show there are distinct differences between seminal publications and literature reviews in terms of author similarity and the strength of ties between their authors. In particular, we find that seminal publications tend to be written by authors who have previously worked on dissimilar problems (i.e. authors from different fields or even disciplines), and by authors who are not frequent collaborators. On the other hand, literature reviews in our dataset tend to be the result of an established collaboration within a discipline. This demonstrates that our method provides meaningful information about potential future impacts of a publication which does not require citation information

Research Collaboration Analysis Using Text and Graph Features

Patterns of scientific collaboration and their effect on scientific production have been the subject of many studies. In this paper we analyze the nature of ties between co-authors and study collaboration patterns in science from the perspective of semantic similarity of authors who wrote a paper together and the strength of ties between these authors (i.e. how much have they previously collaborated together). These two views of scientific collaboration are used to analyze publications in the TrueImpactDataset [11], a new dataset containing two types of publications - publications regarded as seminal and publications regarded as literature reviews by field experts. We show there are distinct differences between seminal publications and literature reviews in terms of author similarity and the strength of ties between their authors. In particular, we find that seminal publications tend to be written by authors who have previously worked on dissimilar problems (i.e. authors from different fields or even disciplines), and by authors who are not frequent collaborators. On the other hand, literature reviews in our dataset tend to be the result of an established collaboration within a discipline. This demonstrates that our method provides meaningful information about potential future impacts of a publication which does not require citation information

Introducing CitedReferencesExplorer (CRExplorer): A program for Reference Publication Year Spectroscopy with Cited References Standardization

We introduce a new tool - the CitedReferencesExplorer (CRExplorer, www.crexplorer.net) - which can be used to disambiguate and analyze the cited references (CRs) of a publication set downloaded from the Web of Science (WoS). The tool is especially suitable to identify those publications which have been frequently cited by the researchers in a field and thereby to study for example the historical roots of a research field or topic. CRExplorer simplifies the identification of key publications by enabling the user to work with both a graph for identifying most frequently cited reference publication years (RPYs) and the list of references for the RPYs which have been most frequently cited. A further focus of the program is on the standardization of CRs. It is a serious problem in bibliometrics that there are several variants of the same CR in the WoS. In this study, CRExplorer is used to study the CRs of all papers published in the Journal of Informetrics. The analyses focus on the most important papers published between 1980 and 1990.Comment: Accepted for publication in the Journal of Informetric

Implementation of Paper Genealogy in Subgraph Mining

Information networks contains many data base in the different search of area , Whenever a new researcher goes to search a topic , there are lots of papers , In those papers some are relevant to user define topic and some are unfamiliar to that topic.For making literature survey researcher needs to collect all information regarding domain which are relevant to that particular topic but there are many citations are available which contains huge amount of data where number of papersis presented by authors.It is very difficult to study all published papers, after analysing this problem an idea is created to solve the problem of search of all research papers with their citation. This paper is design to solve these entire problems, how to find out relative papers with respected query. This paper will be centred on creation of genealogy of all those published papers which will find out the all relevant papers according to user entered keyword it is startingworking of process, after that extraction part will be come in which discrimination of survey paper and implementation of paper will be extracting according to seminal papers it will create genealogy of those paper, by association and interlinking among all matching documents on the basis of references of each paper. The created Genealogy willhelpful for user to get a quick look of their searched topic at which papers are relevant to given query of research, So that all the seminal papers will be shown to user and usercan focus on only those documents . By this proposed work user neither looks on unwanted documents nor expend the time for searching the particular topic, which may increases scalability and efficiency of searching keywords