TY - GEN
T1 - On the extraction and analysis of a social network with partial organizational observation
AU - Whitsitt, Sean
AU - Gopalan, Abishek
AU - Cho, Sangman
AU - Sprinkle, Jonathan
AU - Ramasubramanian, Srinivasan
AU - Suantak, Liana
AU - Rozenblit, Jerzy
PY - 2012
Y1 - 2012
N2 - The behavior of an organization may be inferred based on the behavior of its members, their contacts, and their connectivity. One approach to organizational analysis is the construction and interpretation of a social network graph, where entities of an organization (persons, vehicles, locations, events, etc.) are nodes, and edges represent varying kinds of connectivity between entities. This paper describes a transformation based approach to the extraction of a social network graph, where the original data comprising (partial) observation of the organization are embedded on a graph with a different ontology, and with many entities and edges that are unrelated to the organization of interest. Social network extraction allows the inference of implied relationships, and the selection of relationships relevant for intended analysis techniques. The analysis of the resulting social network graph is based on organizational and individual analysis, in order to permit an advanced user to draw conclusions regarding the behavior of the organization, based on established social network graph metrics. The results of the paper include a discussion of the complexity of analysis, and how the observation data graph is pruned in order to scale the application of analysis algorithms.
AB - The behavior of an organization may be inferred based on the behavior of its members, their contacts, and their connectivity. One approach to organizational analysis is the construction and interpretation of a social network graph, where entities of an organization (persons, vehicles, locations, events, etc.) are nodes, and edges represent varying kinds of connectivity between entities. This paper describes a transformation based approach to the extraction of a social network graph, where the original data comprising (partial) observation of the organization are embedded on a graph with a different ontology, and with many entities and edges that are unrelated to the organization of interest. Social network extraction allows the inference of implied relationships, and the selection of relationships relevant for intended analysis techniques. The analysis of the resulting social network graph is based on organizational and individual analysis, in order to permit an advanced user to draw conclusions regarding the behavior of the organization, based on established social network graph metrics. The results of the paper include a discussion of the complexity of analysis, and how the observation data graph is pruned in order to scale the application of analysis algorithms.
KW - Graph Translation
KW - Network Analysis
KW - Social Networking
UR - http://www.scopus.com/inward/record.url?scp=84862089611&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84862089611&partnerID=8YFLogxK
U2 - 10.1109/ECBS.2012.33
DO - 10.1109/ECBS.2012.33
M3 - Conference contribution
AN - SCOPUS:84862089611
SN - 9780769546643
T3 - Proceedings - 2012 IEEE 19th International Conference and Workshops on Engineering of Computer-Based Systems, ECBS 2012
SP - 249
EP - 256
BT - Proceedings - 2012 IEEE 19th International Conference and Workshops on Engineering of Computer-Based Systems, ECBS 2012
T2 - 2012 IEEE 19th International Conference and Workshops on Engineering of Computer-Based Systems, ECBS 2012
Y2 - 11 April 2012 through 13 April 2012
ER -