197,871 research outputs found
Prediction of scientific collaborations through multiplex interaction networks
Link prediction algorithms can help to understand the structure and dynamics
of scientific collaborations and the evolution of Science. However, available
algorithms based on similarity between nodes of collaboration networks are
bounded by the limited amount of links present in these networks. In this work,
we reduce the latter intrinsic limitation by generalizing the Adamic-Adar
method to multiplex networks composed by an arbitrary number of layers, that
encode diverse forms of scientific interactions. We show that the new metric
outperforms other single-layered, similarity-based scores and that scientific
credit, represented by citations, and common interests, measured by the usage
of common keywords, can be predictive of new collaborations. Our work paves the
way for a deeper understanding of the dynamics driving scientific
collaborations, and provides a new algorithm for link prediction in multiplex
networks that can be applied to a plethora of systems
Network Analysis of Scientific Collaboration and Co-authorship of the Trifecta of Malaria, Tuberculosis and Hiv/aids in Benin.
Despite the international mobilization and increase in research funding, Malaria, Tuberculosis and HIV/AIDS are three infectious diseases that have claimed more lives in sub Saharan Africa than any other place in the World. Consortia, research network and research centers both in Africa and around the world team up in a multidisciplinary and transdisciplinary approach to boost efforts to curb these diseases. Despite the progress in research, very little is known about the dynamics of research collaboration in the fight of these Infectious Diseases in Africa resulting in a lack of information on the relationship between African research collaborators. This dissertation addresses the problem by documenting, describing and analyzing the scientific collaboration and co-authorship network of Malaria, Tuberculosis and HIV/AIDS in the Republic of Benin.
We collected published scientific records from the Web Of Science over the last 20 years (From January 1996 to December 2016). We parsed the records and constructed the coauthorship networks for each disease. Authors in the networks were represented by vertices and an edge was created between any two authors whenever they coauthor a document together. We conducted a descriptive social network analysis of the networks, then used mathematical models to characterize them. We further modeled the complexity of the structure of each network, the interactions between researchers, and built predictive models for the establishment of future collaboration ties. Furthermore, we implemented the models in a shiny-based application for co-authorship network visualization and scientific collaboration link prediction tool which we named AuthorVis.
Our findings suggest that each one of the collaborative research networks of Malaria, HIV/AIDS and TB has a complex structure and the mechanism underlying their formation is not random. All collaboration networks proved vulnerable to structural weaknesses. In the Malaria coauthorship network, we found an overwhelming dominance of regional and international contributors who tend to collaborate among themselves. We also observed a tendency of transnational collaboration to occur via long tenure authors. We also find that TB research in Benin is a low research productivity area. We modeled the structure of each network with an overall performance accuracy of 79.9%, 89.9%, and 93.7% for respectively the malaria, HIV/AIDS, and TB coauthorship network.
Our research is relevant for the funding agencies operating and the national control programs of those three diseases in Benin (the National Malaria Control Program, the National AIDS Control Program and the National Tuberculosis Control Program)
Data-driven modeling of collaboration networks: A cross-domain analysis
We analyze large-scale data sets about collaborations from two different
domains: economics, specifically 22.000 R&D alliances between 14.500 firms, and
science, specifically 300.000 co-authorship relations between 95.000
scientists. Considering the different domains of the data sets, we address two
questions: (a) to what extent do the collaboration networks reconstructed from
the data share common structural features, and (b) can their structure be
reproduced by the same agent-based model. In our data-driven modeling approach
we use aggregated network data to calibrate the probabilities at which agents
establish collaborations with either newcomers or established agents. The model
is then validated by its ability to reproduce network features not used for
calibration, including distributions of degrees, path lengths, local clustering
coefficients and sizes of disconnected components. Emphasis is put on comparing
domains, but also sub-domains (economic sectors, scientific specializations).
Interpreting the link probabilities as strategies for link formation, we find
that in R&D collaborations newcomers prefer links with established agents,
while in co-authorship relations newcomers prefer links with other newcomers.
Our results shed new light on the long-standing question about the role of
endogenous and exogenous factors (i.e., different information available to the
initiator of a collaboration) in network formation.Comment: 25 pages, 13 figures, 4 table
From sand to networks: a study of multi-disciplinarity
In this paper, we study empirically co-authorship networks of neighbouring
scientific disciplines, and describe the system by two coupled networks. By
considering a large time window, we focus on the properties of the interface
between the disciplines. We also focus on the time evolution of the
co-authorship network, and highlight a rich phenomenology including first order
transition and cluster bouncing and merging. Finally, we present a ferro-
electric-like model (CDIM), involving bond redistribution between the nodes,
that reproduces qualitatively the structuring of the system in homogeneous
phasesComment: submitted to europhys. let
AUGUR: Forecasting the Emergence of New Research Topics
Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall
Towards journalometrical analysis of a scientific periodical: a case study
In this paper we use several approaches to analyse a scientific journal as a
complex system and to make a possibly more complete description of its current
state and evolution. Methods of complex networks theory, statistics, and
queueing theory are used in this study. As a subject of the analysis we have
chosen the journal ``Condensed Matter Physics''
(http://www.icmp.lviv.ua/journal/). In particular, based on the statistical
data regarding the papers published in this journal since its foundation in
1993 up to now we have composed the co-authorship network and extracted its
main quantitative characteristics. Further, we analyse the priorities of
scientific trends reflected in the journal and its impact on the publications
in other editions (the citation ratings). Moreover, to characterize an
efficiency of the paper processing, we study the time dynamics of editorial
processing in terms of queueing theory and human activity analysis
Structural constraints in complex networks
We present a link rewiring mechanism to produce surrogates of a network where
both the degree distribution and the rich--club connectivity are preserved. We
consider three real networks, the AS--Internet, the protein interaction and the
scientific collaboration. We show that for a given degree distribution, the
rich--club connectivity is sensitive to the degree--degree correlation, and on
the other hand the degree--degree correlation is constrained by the rich--club
connectivity. In particular, in the case of the Internet, the assortative
coefficient is always negative and a minor change in its value can reverse the
network's rich--club structure completely; while fixing the degree distribution
and the rich--club connectivity restricts the assortative coefficient to such a
narrow range, that a reasonable model of the Internet can be produced by
considering mainly the degree distribution and the rich--club connectivity. We
also comment on the suitability of using the maximal random network as a null
model to assess the rich--club connectivity in real networks.Comment: To appear in New Journal of Physics (www.njp.org
Interplay between network structure and self-organized criticality
We investigate, by numerical simulations, how the avalanche dynamics of the
Bak-Tang-Wiesenfeld (BTW) sandpile model can induce emergence of scale-free
(SF) networks and how this emerging structure affects dynamics of the system.
We also discuss how the observed phenomenon can be used to explain evolution of
scientific collaboration.Comment: 4 pages, 4 figure
Fractional Dynamics of Network Growth Constrained by aging Node Interactions
In many social complex systems, in which agents are linked by non-linear
interactions, the history of events strongly influences the whole network
dynamics. However, a class of "commonly accepted beliefs" seems rarely studied.
In this paper, we examine how the growth process of a (social) network is
influenced by past circumstances. In order to tackle this cause, we simply
modify the well known preferential attachment mechanism by imposing a time
dependent kernel function in the network evolution equation. This approach
leads to a fractional order Barabasi-Albert (BA) differential equation,
generalizing the BA model. Our results show that, with passing time, an aging
process is observed for the network dynamics. The aging process leads to a
decay for the node degree values, thereby creating an opposing process to the
preferential attachment mechanism. On one hand, based on the preferential
attachment mechanism, nodes with a high degree are more likely to absorb links;
but, on the other hand, a node's age has a reduced chance for new connections.
This competitive scenario allows an increased chance for younger members to
become a hub. Simulations of such a network growth with aging constraint
confirm the results found from solving the fractional BA equation. We also
report, as an exemplary application, an investigation of the collaboration
network between Hollywood movie actors. It is undubiously shown that a decay in
the dynamics of their collaboration rate is found, - even including a sex
difference. Such findings suggest a widely universal application of the so
generalized BA model.Comment: 13 pages; 5 figures; 71 references; as prepared for submission to
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