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Network community based on symbolic analysis method

Author: ZuoHongJun
Tutor: ZuoBinBin
School: Huazhong University of Science and Technology
Course: Computer Software and Theory
Keywords: Symbols network Community divided Boundary symbol prediction
CLC: TP393.0
Type: Master's thesis
Year: 2011
Downloads: 34
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Abstract


With the development of information technology , social network analysis applications have been limited to small groups of traditional social relations , but extends to almost all areas of human endeavor , covering social , economic, political , information science , and many other directions . Knowledge of computer science as an auxiliary tool for the social network dataset for storage , retrieval and analysis, has become a hot research topic. Existing social network analysis is often used a single positive relationship boundary value model to represent the relationship between nodes , while the social networks because of widespread opposition between boundary value problems arising from the lack of a sign -depth study . Therefore, it is necessary to contain the symbol side of the social networks provide effective methods of analysis. Symbols network community structure analysis methods mainly from the community relations division and boundary value predicted two aspects of research . K- symbol-based core network partitioning strategy for symbolic characteristics of the network , through social structural balance theory model to community nodes to determine the initial network symbolic density clusters , and for the second iteration of relocations. Compared with the random division , effectively reducing the number of exchange nodes to improve the division results. Meanwhile in the common neighbors metric prediction algorithm based on comprehensive consideration of the network node symbols preference theory , an improved network edge symbolic value prediction method to avoid the single common neighbor selection algorithm predicted value sensitivity , allowing the symbol unknown boundary value network and missing boundary value judgment to make more accurate forecasts . Through the establishment of the network simulation community of the symbol boundary value partitioning strategy and forecasting method was experimentally verified. Experimental results show that the two methods are in their evaluation of the performance can be improved and network data analysis in the symbol obtained in better application .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues
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