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Funding Networks. Abdullah Sevincer University of Nevada, Reno Department of Computer Science & Engineering. Agenda. Motivation & Study Introduction Background & Related Work Conclusion Questions?. Motivation & Study.
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Funding Networks Abdullah Sevincer University of Nevada, Reno Department of Computer Science & Engineering
Agenda • Motivation & Study • Introduction • Background & Related Work • Conclusion • Questions?
Motivation & Study • The 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 & Study • Using 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
Introduction • Better understanding of the structure of the network • Who is the most outstanding?
Introduction • Present 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
Introduction • Help 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.
Introduction • Locate, 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 Work • There 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 Work • Cotta, 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 Work • What 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 Work • Li, 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 Work • They 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 Work • Patent 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 Work • Zhang, 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 Work • All 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 & Summary • Discover 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 & Summary • Find who is the most outstanding, who is at the bottom of the line. • Who is central? • Closeness and betweennesscentrality? • 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 & Summary • Whether 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)?
