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- 저자 : ( He Tingqin ) (검색결과 3 건)
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K-Hop Community Search Based On Local Distance Dynamics
( Tao Meng ),( Lijun Cai ),( Tingqin He ),( Lei Chen ),( Ziyun Deng ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.7
Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric. This problem has recently drawn intense research interest. However, most metric-based algorithms tend to include irrelevant subgraphs in the identified community. Apart from the user-defined metric algorithm, how can we search the natural community that the query node belongs to? In this paper, we propose a novel community search algorithm based on the concept of the k-hop and local distance dynamics model, which can naturally capture a community that contains the query node. The basic idea is to envision the nodes that k-hop away from the query node as an adaptive local dynamical system, where each node only interacts with its local topological structure. Relying on a proposed local distance dynamics model, the distances among nodes change over time, where the nodes sharing the same community with the query node tend to gradually move together, while other nodes stay far away from each other. Such interplay eventually leads to a steady distribution of distances, and a meaningful community is naturally found. Extensive experiments show that our community search algorithm has good performance relative to several state-of-the-art algorithms.
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Enhanced Distance Dynamics Model for Community Detection via Ego-Leader
( Cai Lijun ),( Zhang Jing ),( Chen Lei ),( He Tingqin ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.5
Distance dynamics model is an excellent model for uncovering the community structure of a complex network. However, the model has poor robustness. To improve the robustness, we design an enhanced distance dynamics model based on Ego-Leader and propose a corresponding community detection algorithm, called E-Attractor. The main contributions of E-Attractor are as follows. First, to get rid of sensitive parameter λ, Ego-Leader is introduced into the distance dynamics model to determine the influence of an exclusive neighbor on the distance. Second, based on top-k Ego-Leader, we design an enhanced distance dynamics model. In contrast to the traditional model, enhanced model has better robustness for all networks. Extensive experiments show that E-Attractor has good performance relative to several state-of-the-art algorithms.
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Ziyun Deng,Jing Zhang,Lijun Cai,Lei Chen,Tingqin He 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.9
When Cloud Platform for Computer Aided Engineering Simulation (CPCAES) are developed to use Service-Oriented Architecture (SOA), on the one hand, it’s necessary to accurately express the service requirements of users, on the other hand it’s need to semantic of the existing services in the platform, and then to match the ontologies. The existing Ontology Web Language for Services / Universal Description Discovery and Integration (OWL-S / UDDI) algorithm has weak matching precision, and not meet the Computer Aided Engineering (CAE) simulation applications. The other semantic similarity algorithms also have weakness in judgment factor, or do not meet the requirements of CAE simulation applications for matching to service requirement ontology in cloud platform. Such that, the authors present a kind of ontology of service requirement for CAE simulation, model the content of the ontology, including resource requirements, computing requirements, computing job requirements, input, output, etc. The authors give the ontology mapping relationship between Ontology Web Language for Services (OWL-S) and the ontology, give the matching decision rules for the ontologies, propose a matching algorithm for matching the ontologies, and compare with the classic OWL-S / UDDI matching algorithm and a similarity matching algorithm proposed in other paper in the ability for measure the similarity quantity of services, the ability to suit the applications, as well as algorithms recall rate and precision rate. The results show the matching algorithm proposed in this paper is more suitable for service requirement ontology of CPCAES, can use the quantify value for the similarity analysis to the ontologies, and has higher precision rate and higher recall rate, which can reach about 90%. The research work in this paper is used in the second prototype of CPCAES, and the research team is developing the third prototype based on semantic Web Services and SOA framework.
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