TY - JOUR
T1 - Visualizing social network concepts
AU - Zhu, Bin
AU - Watts, Stephanie
AU - Chen, Hsinchun
N1 - Funding Information:
Dr. Hsinchun Chen is the McClelland professor of Management Information Systems and Andersen professor of MIS at the University of Arizona, where he is the director of the Artificial Intelligence Lab and the director of the Hoffman E-Commerce Lab. He received his PhD degree in Information Systems from the New York University in 1989. His articles have appeared in Communications of ACM, ACM Transactions on Information Systems, IEEE Computer, Journal of the American Society for Information Science and Technology, Decision Support Systems, and many other journals. Professor Chen has received grant awards from NSF, DARPA, NASA, NIH, NIJ, NLM, NCSA, HP, SAP, 3COM, and AT&T. He serves on the editorial board of Decision Support Systems, Journal of American Society for Information Science and Technology, and ACM Transactions on Information System.
Funding Information:
The authors would like to thank detective Tim Peterson from the Tucson Police Department for taking time to interview with us and providing valuable domain knowledge about the social network data used in this research. This research is sponsored by the Boston University Institute for Leading in a Dynamic Economy (BUILDE) .
PY - 2010/5
Y1 - 2010/5
N2 - Social network concepts are invaluable for understanding the social network phenomena, but they are difficult to comprehend without computerized visualization. However, most existing network visualization techniques provide limited support for the comprehension of network concepts. This research proposes an approach called concept visualization to facilitate the understanding of social network concepts. The paper describes an implementation of the approach. Results from a controlled laboratory experiment indicate that, compared with the benchmark system, the NetVizer system facilitated better understanding of the concepts of betweenness centrality, gatekeepers of subgroups, and structural similarity. It also supported a faster comprehension of subgroup identification.
AB - Social network concepts are invaluable for understanding the social network phenomena, but they are difficult to comprehend without computerized visualization. However, most existing network visualization techniques provide limited support for the comprehension of network concepts. This research proposes an approach called concept visualization to facilitate the understanding of social network concepts. The paper describes an implementation of the approach. Results from a controlled laboratory experiment indicate that, compared with the benchmark system, the NetVizer system facilitated better understanding of the concepts of betweenness centrality, gatekeepers of subgroups, and structural similarity. It also supported a faster comprehension of subgroup identification.
KW - Information analysis
KW - Information categorization
KW - Network visualization
KW - Social network analysis
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U2 - 10.1016/j.dss.2010.02.001
DO - 10.1016/j.dss.2010.02.001
M3 - Article
AN - SCOPUS:77951140110
SN - 0167-9236
VL - 49
SP - 151
EP - 161
JO - Decision Support Systems
JF - Decision Support Systems
IS - 2
ER -