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The importance of link-based prediction algorithm for dynamic linking

Author: LiDongCai
Tutor: LiYuHua
School: Huazhong University of Science and Technology
Course: Applied Computer Technology
Keywords: Scientific collaboration networks Link prediction Importance of Links Semantic similarity Dynamic
CLC: TP393.03
Type: Master's thesis
Year: 2011
Downloads: 44
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Abstract


As a social network , scientific collaboration network entity relationship is between the two co- authors published a paper . Scientific collaboration networks is an important issue is to predict the cooperation between the two authors of papers published case in data mining , the issue that is linked forecasts. In scientific collaboration network link prediction problem , the traditional methods are generally calculated based on graph or a combination of the topological properties of the classification of semantic attributes , but existing methods have two major problems: a thesis in computer representation of the formation of multi- network Article link is often treated equally , but the reality is different authors on a paper linked contribution is not the same . Another problem is related to the property in the calculation process , will be treated the same historical data , ignoring the time factor in the impact of the link formation process , obviously this is not consistent with the facts. For the above two points , given the importance of a link-based dynamic link prediction algorithm . According to Digital Bibliography Library Project (DBLP) the characteristics of the data set , the data set a preprocessing, including connected component and two filtering rules for solving a filter , so that the size of the data reduction and data noise reduction , it is a good improve forecast accuracy and reduce the time complexity. Pretreatment, given the importance of the measure called links , and thus measure based on the topological properties of commonly used correction . In the semantic similarity calculation process, the vector space model and TF-IDF has been improved , and the importance of links based on papers presented contribution to the value of a single metric to measure single papers when describing different authors difference. Combined with modified TF-IDF and single- valued metric contributed papers given semantic similarity calculation formula . Introduction of the time factor on the topological properties of the above definitions and semantic similarity final amended to reflect the time factor on the dynamic link formation. The property is characterized by attributes , the use of classification techniques for the final prediction . In the DBLP data set on multiple sets of comparative experiments . Experimental results show that the algorithm compared with existing methods , predictive accuracy has been greatly improved.

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