funding networks abdullah sevincer university of nevada, reno department of computer science &...
Post on 19-Dec-2015
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Funding Networks
Abdullah Sevincer University of Nevada, Reno
Department of Computer Science & Engineering
Motivation & StudyThe funding from the government
agencies has been the driving force for the research an educational institutes.
The data of funding is available to public.
The institutes, authors and co-authors of funding information forms a complex network.
Motivation & StudyUsing the funding data collected
from the government agencies discover the complex network of funding.
Explore the features of this complex network by applying complex network theories.
Introduction
Complex networks is a young and active area of scientific research inspired largely by the empirical study of real-world networks such as computer networks and social networks.
Complex network theory of information a reveals the structure of a complex network from a data set which stays as a statistical information
IntroductionPresent the data set in complex
network form to infer the complex network properties of the data.
Using statistical models doesn’t help.
Data: The funding from the government agencies.
Introduction
The information is statistical.
Data contains all of the information.
Collect this data set and and apply complex network theory.
Derive new characteristics
IntroductionHelp government to distribute
fund properly.Discover the properties of
funding network.Combine or collaborate
redundant research topics based upon relationship between researchers and research topic.
IntroductionLocate, collect and organize the
data.The data collection technique is
manual.Use local data base for the data
storage.Custom developed tool to
generate network file.Visualize the network data using
network visualization tools.
Background & Related WorkThere hasn’t been a study related
to Research Funding Network in Complex Network area.
Similar work includes people in a social network such as authors network legal citation network or citation network for patent classification.
Background & Related WorkCotta, et. al., Explores the network of authors of
evolutionary computation papers found in a major bibliographic data base.
Compare this network with the other co-authorship networks and explore some distinctive properties of this network
Background & Related WorkWhat kind of macroscopic values
the network yield?Which are the most outstanding
actors (authors) and edges (co-authors) within the network?
Who are the central authors in the network and what determines their prominency in the area.
Background & Related WorkLi, et. al., Use patent citation information
and network to address the patent classification problem.
Adopt a kernel based approach and design kernel functions to capture content information and various citation related information in patents.
Background & Related WorkThey show that proposed labeled
citation graph kernel with utilization of citation networks outperforms the one that uses no citation or only direct citation information.
Background & Related WorkPatent application: appropriate patent
examiner-(assigning)categories in patent classification scheme.
The classification of patents are very important and labor task since the patent applications increase by year.
Manual classification of patents is labor intensive and time consuming.
The previous methods are not efficient to classify the patents into categories.
Background & Related WorkZhang, et. al., Present Semantics based legal
citation network Viewer as a research tool for legal professionals.
The viewer accurately traces a given legal issue in past and subsequent cases along citation links, and gives the user a visual image of how the citation on the same issue are interrelated.
Background & Related WorkAll the background can be associated
to proposed research funding network in one way to another.
They are different in structure and scale of the network.
They don’t fit for the required network with limitations and different analysis.
The funding network forms a different complex network with its own features and relations.
Conclusions & SummaryDiscover the complex network of
funding.Collect the data, organize and
apply complex network theories to better understand and explore the distinctive specifications of Funding Network.
Compare with other networks find the similarities and differences.
Conclusions & SummaryFind who is the most outstanding,
who is at the bottom of the line.Who is central?Closeness and betweenness
centrality?How researchers and institutions are
connected via grants?What is the density (i.e. clustering) of
funding networks and how it differs with different year and research field?
Conclusions & SummaryWhether researcher and institutions
form assortativity in their collaborations?Whether there is a rich club among
institutions or researchers?How social network characteristics of
funding networks change over time?Whether different research fields have
different characteristics?Whether there are different patterns in
different funding levels (e.g. 0-1K,1K-0.5M, 0.5M-1M)?