Research And Application Of Social Network Analysis Based On Civil Aviation Travel Relationship

Social networks are ubiquitous,and their essence is to connect different entities to form the whole with common characteristics.In recent years,with the rapid development of the aviation industry,and the number of civil aviation passengers show a trend of rapid growth.The author works in government agencies and has more contacts with airlines,so by managing travel information of the large amount of passengers,setting up travel social networks,analyzing passenger behavior traces and the relationship between passengers,and providing targeted caring services and marketing for influential passengers,it can give relevant business departments proper approach and right decision support,which is conducive to improving the operational efficiency of the airlines and bringing economic benefits and huge commercial value.On the basis of analyzing the trip record of passengers and model of behavior analysis model(RFM model),together with the travel data of civil aviation,the author of the thesis constructs the travel social network of civil aviation in daily work.In the RFM model,passengers are the network node and the travel relationship between the passengers is the edge of the network.At the same time,according to the closeness of the travel relationship between passengers,weight is given to each side of travel social network.And then,this thesis builds the travel social network of civil aviation on the basis of some real travel data.In the travel social network of civil aviation,some passengers are of paramount influence,importance,and value.Such passengers are the center of the travel social network of civil aviation and have a strong impact on other passengers in the network.The identification of key passengers is of application value and social benefits.This thesis uses degree centrality algorithm,intermediate centrality algorithm,third-order friend number algorithm and PageRank algorithm to identify key passengers in the travel social network of civil aviation.At the same time,this thesis experiments the four algorithms mentioned above on different social networks.Experiments show that different key passenger recognition algorithms have different effects on different networks,and PageRank algorithm has better performance.In addition,the thesis also analyzes and discusses passengers’ seat selection preferences and luggage preferences.Experiments prove the effectiveness of the preference algorithm in this thesis,which can better describe passengers’ preference for seats and luggage,provide a theoretical basis for airlines’ targeted marketing,and can improve passenger satisfaction in practical work.The innovations of this thesis are as follows.First,the classic PageRank algorithm does not consider the importance of links when calculating.The classic PageRank algorithm is improved here,and combined with the characteristics of civil aviation social networks,when calculating PageRank,the edge weights of social networks are also involved in the calculation.Secondly,the third-order friend number algorithm is innovatively aligned with the key passenger identification algorithm,and the effectiveness of the third-order friend number algorithm in key passenger identification is verified.In the analysis,there are few literatures that analyze passenger baggage preferences.And here is an analysis of passengers’ baggage preferences,which has certain application value.