Research And Analysis Of Alumni Network Based On Theory Of Complex Network

Today is the era of the information age. With the rapid development ofinformation industry, complex networks have becoming an emerging and researchingdiscipline in recently years. In the progress of human society, a lot of practicalproblems in nature and real human society that can be expressed with the networktopology and that are used complex network theory to solve these problems, whichare more systematic and intuitive. Studying of complex networks in-depth can helppeople better understand the network and make the network better serve mankind andpush forward the development of complex networks disciplines.Firstly, alumni information provided by the Qingdao Technological UniversityAlumni Association database is preprocessed. According to the characteristics of thevarious college professional settings in the university and the important role ofmonitor group secretary and other class leadership playing in the class, severalrepresentative properties selected from a number of alumni data properties, learningthe space L method and the space P method in public transport network model tobuild ANL alumni network and ANP alumni network. Then, according to the buildingANL and ANP alumni network to analyze average path length, clustering coefficientand degree distribution basic network characteristics, the simulation results show thatthe ANL alumni network and ANP alumni network possess the basic properties ofcomplex networks.Secondly, analyzed the complex network assessment methods and applied to thealumni network, verifying the importance of monitor, group secretary and other classleadership in the alumni network and classes; Vital nodes evaluation method based onmutual information has been improved on the original algorithm to calculate thenodes which are the amount of information that greater than the average node degree,shortening the time to calculate the amount of information and improving theoperating efficiency of the algorithm. Finally, the classical community division algorithms have been analyzed, thecomplex networks clustering algorithm based on mutual information has beenimproved and verified, the improved algorithm solves the problem of how to select N.The chapter puts forward the concept of community strength coefficient and improvescommunity structure detection algorithm and puts forward a kind of communitystructure detection algorithm based on community strength coefficient. The improvedalgorithm has been verified and applied by means of classical network. Thesimulation results show that the algorithm has a lower time complexity and betterfeasibility and versatility to divide community structure.The paper constructs alumni network, verifies and analyses the basic propertiesof complex networks, and proposes community structure detection algorithm based oncommunity strength coefficient. Which applied to the alumni network, the network isdivided into a number of communities, proving that the important role of monitorgroup secretary and other class leadership in the class.