Research On Heterogeneous Information Network Analysis Model And Application

In information network, a new heterogeneous trend comes true with thedevelopment of information diversification and complex relationships betweeninformation objects. Faced on the heterogeneous network structure, how to find theuseful knowledge and to improve the utilization of information based on networkanalysis is one of most urgent problems. In the heterogeneous information world, data,services, and human activities are the key engagement elements consisting of the usageof information. Relational database model can provide structured storage andmanagement format for the mass of information data; Web services as the representativefunction package and development technology can construct open, loosely coupledinformation platform; Social network provides the open platform for informationsharing and dissemination. With the diversification of data categories, frequent access toweb services and the rapid development of the social network, there is a growingdemand for information sorting and knowledge mining from the heterogeneous forms ofdata network, service network, and social network.In this paper, with the analysis of current information network and heterogeneousinformation network, faced on the problems of current researches, we mined therelationship between the heterogeneous objects in heterogeneous information network,studied of heterogeneous information network description model deeply, studied theheterogeneous information network description model based ranking functions whichconsider the linkage analysis of network structure. From the dimension of therelationship, the new heterogeneous information network analysis model basedclustering analysis, ranking and activity prediction methods are proposed. As the casestudy, we finished some basic researches and provide specific solutions for problems ofservices heterogeneous network, relational database tuple networks, social network.The details of research works in this paper include:①We analyzed the trends and heterogeneous characteristics of currentinformation network development, analyzed current situation and existing problems inclustering, personal query and social network prediction, Studied the format descriptionof heterogeneous information network description model.②Based on the heterogeneous information network description model, several network analysis based ranking functions are studied, considering the different forms ofnetwork connectivity and ranking rules; Through the comparison and analysis ofranking results, provide method support for network analysis based ranking.③Faced on the problems of property computing based clustering researches, formthe view of relationships we proposed a novel clustering algorithm based onheterogeneous information network analysis, and studied the basic idea and process ofthe proposed clustering algorithm; as special case of heterogeneous informationnetwork applications, in order to solve the problems of web services clustering, weproposed a new clustering algorithm based on service tags considered network structureand heterogeneous service network analysis, co-considering the clustering and rankingprocess of services, ranking model provides compute vectors for clustering process andfinish the ranking results in different service clusters. In order to evaluate theperformance and accuracy, we designed experiments with the true web services datasetfrom Titan.④In order to improve the personalized query support of in information searchesand queries, we proposed a personalized query method based on heterogeneousinformation network analysis which mines the possible ranking results considering thehidden categories. As the case study of specific network, we provided a new rankingalgorithm for personal top-k query in relational database which analyzes the foreignkeys linked tuple relations and schemas, study the extraction method of heterogeneoustuple network. Based on the relational tuples network structure, researched the rankingmodel and process of proposed algorithm. The ranking algorithm of relational databasecan classified as single category ranking and ranking consider multi-classes in whichthe latter ranking should consider the potential categories hidden behind data. Theexperiment analysis part chooses real databases from IMDB and German Credit toevaluate the proposed ranking algorithm on performance and accuracy.⑤Based on the means of heterogeneous information network analysis, combinedwith the social network activity forecast demand, we proposed social network activitiesprediction model based on heterogeneous information network analysis. Consideringthe analysis of network structure and messages propagation in social network, weproposed four different prediction models for message re-tweet and possible view times.Combining the proposed four prediction models with different weights based on accuracies of prediction, we constructed a composite prediction model. The experimentswith real Weibo data give an evaluation on available and accuracy of research.