NSF Award Abstract - #0324947| NSF Award Abstract - #0324947 |
AWSFL008-DS3 |
| NSF Org | IIS |
| Latest Amendment Date | July 21, 2004 |
| Award Number | 0324947 |
| Award Instrument | Continuing grant |
| Program Manager |
C. Suzanne Iacono IIS DIV OF INFORMATION & INTELLIGENT SYSTEMS CSE DIRECT FOR COMPUTER & INFO SCIE & ENGINR |
| Start Date | September 15, 2003 |
| Expires | August 31, 2007 (Estimated) |
| Expected Total Amount | $550000 (Estimated) |
| Investigator |
Mark K. Goldberg goldberg@cs.rpi.edu (Principal Investigator current) Bulent Yener (Co-Principal Investigator current) William A. Wallace (Co-Principal Investigator current) Malik Magdon-Ismail (Co-Principal Investigator current) Mukkai S. Krishnamoorthy (Co-Principal Investigator current) |
| Sponsor |
Rensselaer Polytech Inst 110 8th Street Troy, NY 121803522 518/276-6000 |
| NSF Program | 1687 ITR MEDIUM (GROUP) GRANTS |
| Field Application |
0116000 Human Subjects 0104000 Information Systems |
| Program Reference Code | 1657,9218,HPCC, |
This project will develop novel methods for the statistical analysis of the evolution of social groups acting within a large social network via a communications network, such as the Internet. The basis for the analysis is a set of probabilitistic laws (micro-laws) that govern the individual behavior of actors, which largely determines the macro-evolution of the social groups. The parameters of the macro-evolution can be measured and then used to determine, via reverse engineering, the parameters of the micro-laws that fit the observed evolution. Understanding the evolution of social communities will help determine resource allocation strategies for enhancing the functioning of these communities. The methods developed in this research can be used to locate, within a large social network, social groups that try to hide their inner communications. Such a system can be instrumental in preventing terrorists' attacks similar to those on September 11, 2001. The proposed research will also open up novel educational opportunities at the home university related to social network analysis.
