DocumentCode :
1852778
Title :
The Analysis of Knowledge Transfer Network Characteristic Based on Small-world Network Model
Author :
Bo, Yang ; Sheng-hua, XU
Author_Institution :
Sch. of Inf. Manage., Univ. of Finance & Econ., Nanchang, China
fYear :
2010
fDate :
22-24 Jan. 2010
Firstpage :
428
Lastpage :
432
Abstract :
By analysising Small-world Network Model and its algorithm, we adopt agent adaptability modeling method of Complex Adaptive System and use Multi-Agent development language Netlogo to build simulation model based on Knowledge Transfer Network of Small-world. We use the average path length of Small-world Network Characteristic and clustering coefficient to stand for AC frequency and aggregation degree between network organizations´ network nodes. Operating simulation model means Knowledge Transfer Network has evident Small-world Network Characteristic and confirms that under the condition of Small-world, the efficiency of Agent behavior and organization Knowledge Transfer can reach an upper high standard. It can offer theory director to construct adaptive knowledge communication between network organizations and changeable network construction.
Keywords :
adaptive systems; complex networks; knowledge management; multi-agent systems; Netlogo; adaptive knowledge communication; agent adaptability modeling method; complex adaptive system; knowledge transfer network characteristic; multiagent development; network organizations; small world network model; Adaptive systems; Algorithm design and analysis; Analytical models; Clustering algorithms; Complex networks; Finance; Information analysis; Information management; Knowledge transfer; Social network services; Knowledge Network; Multi-Agent Mod; NetLogo simulation; Small-World Netwok;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Future Networks, 2010. ICFN '10. Second International Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-0-7695-3940-9
Electronic_ISBN :
978-1-4244-5667-3
Type :
conf
DOI :
10.1109/ICFN.2010.15
Filename :
5431805
Link To Document :
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