36,925 research outputs found
Collaboration in sensor network research: an in-depth longitudinal analysis of assortative mixing patterns
Many investigations of scientific collaboration are based on statistical
analyses of large networks constructed from bibliographic repositories. These
investigations often rely on a wealth of bibliographic data, but very little or
no other information about the individuals in the network, and thus, fail to
illustrate the broader social and academic landscape in which collaboration
takes place. In this article, we perform an in-depth longitudinal analysis of a
relatively small network of scientific collaboration (N = 291) constructed from
the bibliographic record of a research center involved in the development and
application of sensor network and wireless technologies. We perform a
preliminary analysis of selected structural properties of the network,
computing its range, configuration and topology. We then support our
preliminary statistical analysis with an in-depth temporal investigation of the
assortative mixing of selected node characteristics, unveiling the researchers'
propensity to collaborate preferentially with others with a similar academic
profile. Our qualitative analysis of mixing patterns offers clues as to the
nature of the scientific community being modeled in relation to its
organizational, disciplinary, institutional, and international arrangements of
collaboration.Comment: Scientometrics (In press
Software tools for conducting bibliometric analysis in science: An up-to-date review
Bibliometrics has become an essential tool for assessing and analyzing the output of scientists, cooperation between
universities, the effect of state-owned science funding on national research and development performance and educational
efficiency, among other applications. Therefore, professionals and scientists need a range of theoretical and practical
tools to measure experimental data. This review aims to provide an up-to-date review of the various tools available
for conducting bibliometric and scientometric analyses, including the sources of data acquisition, performance analysis
and visualization tools. The included tools were divided into three categories: general bibliometric and performance
analysis, science mapping analysis, and libraries; a description of all of them is provided. A comparative analysis of the
database sources support, pre-processing capabilities, analysis and visualization options were also provided in order to
facilitate its understanding. Although there are numerous bibliometric databases to obtain data for bibliometric and
scientometric analysis, they have been developed for a different purpose. The number of exportable records is between
500 and 50,000 and the coverage of the different science fields is unequal in each database. Concerning the analyzed
tools, Bibliometrix contains the more extensive set of techniques and suitable for practitioners through Biblioshiny.
VOSviewer has a fantastic visualization and is capable of loading and exporting information from many sources. SciMAT
is the tool with a powerful pre-processing and export capability. In views of the variability of features, the users need to
decide the desired analysis output and chose the option that better fits into their aims
Name Disambiguation from link data in a collaboration graph using temporal and topological features
In a social community, multiple persons may share the same name, phone number
or some other identifying attributes. This, along with other phenomena, such as
name abbreviation, name misspelling, and human error leads to erroneous
aggregation of records of multiple persons under a single reference. Such
mistakes affect the performance of document retrieval, web search, database
integration, and more importantly, improper attribution of credit (or blame).
The task of entity disambiguation partitions the records belonging to multiple
persons with the objective that each decomposed partition is composed of
records of a unique person. Existing solutions to this task use either
biographical attributes, or auxiliary features that are collected from external
sources, such as Wikipedia. However, for many scenarios, such auxiliary
features are not available, or they are costly to obtain. Besides, the attempt
of collecting biographical or external data sustains the risk of privacy
violation. In this work, we propose a method for solving entity disambiguation
task from link information obtained from a collaboration network. Our method is
non-intrusive of privacy as it uses only the time-stamped graph topology of an
anonymized network. Experimental results on two real-life academic
collaboration networks show that the proposed method has satisfactory
performance.Comment: The short version of this paper has been accepted to ASONAM 201
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