Discovering the Complexity of Research Funding Networks: A Study in Complex Network Theory, Slides of Computer Networks

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Funding Networks

Agenda

• Motivation & Study
• Introduction
• Background & Related Work
• Conclusion
• Questions?

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

• 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

• 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

• 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

• 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.

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?