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 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 & 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)?

Questions