1. T HE S TRUCTURE OF S CIENTIFIC C OLLABORATION N ETWORKS & R ESEARCH F UNDING N ETWORKS CS790g Complex Networks Jigar Patel November 30 th 2009

2. O UTLINE 1. Scientific Collaboration Networks 1. Introduction 2. Results 3. Conclusions 2. Research Funding Networks 1. Project Idea 2. Data Collection Techniques & Problems 3. Noise in Data 4. Data Processing Issues 5. Data/Network Analysis? 3. Summary 4. Questions

3. 1.S CIENTIFIC C OLLABORATION N ETWORKS 1. Introduction What is the idea behind it? Representation as a graph Interest in studying Social Network Stanley Milgram experiment Problems with social network studies Labor intensive and size of the network can be mapped is limited Highly subjective Movie actors network example Scientific Collaboration Networks Data sources (MEDLINE), SPIRES, NCSTRL Data between 1995-1999

4. 1.S CIENTIFIC C OLLABORATION N ETWORKS 2. Results Number of Authors

5. 1.S CIENTIFIC C OLLABORATION N ETWORKS 2. Results Mean Papers per Author and Authors per Paper

6. 1.S CIENTIFIC C OLLABORATION N ETWORKS 2. Results Number of Collaborators

7. 1.S CIENTIFIC C OLLABORATION N ETWORKS 2. Results The Giant Component

8. 1.S CIENTIFIC C OLLABORATION N ETWORKS 2. Results Clustering

9. 2. R ESEARCH F UNDING N ETWORK 1. Project Idea Grant 1 Grant 2 Grant 3 Institute 1 Institute 2 Institute 3 Grant 2 Grant 1 Grant 3 Common Research Topic

10. 2. R ESEARCH F UNDING N ETWORK 2. Data Collection Techniques & Problems Custom Application MySQL Database for local storage Time consuming Limitation on number of queries can be made to server Proxy server issue Data parsing issues Unknown field size

11. 2. R ESEARCH F UNDING N ETWORK 3. Noise in Data Duplicate organization names Multiple entries for the same organization Duplicate awards Too many PIs without an award 4. Data Processing Issues Large dataset 470K+ PIs, 16K+ Organizations, 290K+ awards Takes too many queries to generate network file. Very large dataset for the visualization

12. 2. R ESEARCH F UNDING N ETWORK 5. Data/Network Analysis Complex Network Theories Very abstract properties of the network Average path length, degree distribution, clustering coefficient, giant component size, betweenness, closeness, prestige etc.. Statistical Theories Gives other perspective on data Average money, minimum, maximum, histogram, total amount distributed, median, percentages, timeline etc.. Data analysis by program, organization or combination of one or many factors

13. 3. S UMMARY Social Network Study It is always interesting and helps understand human nature. Reveals the relationship between researchers in different scientific community +Research Funding Network Shows money distribution Statistical side of the data Very interesting and has never been generated

14. Q UESTIONS ?