Research On Community Detection And Its Application In Social Networks

With the explosive development of information technologies,we have already entered the broadband Internet era.As a prosperous application of the Internet,online social networks have satisfied the requirements of society by facilitating the ability of humans from diverse areas to interact and share their experiences.Communities,the sets of individuals with common properties or the same roles,are ubiquitous in social networks with denser connections inside each module and fewer connections crossing modules.Community detection is a procedure for identifying all of the clusters in a social network for social computing,such as computational biology,link prediction,and event detection.These social networking sites not only provide potential benefits to the participants,but also present the challenge of information overload and pose higher requirements on community detection.The design of community detection schemes in large-scale social networks with a heterogeneous structure and the exploration of the relevant applications based on the characteristics of social media have become a challenging problem demanding prompt solutions.This paper aims at conquering this difficult task with effective and efficient algorithms;in particular,our contributions can be summarized as follows.(1)To cluster individuals with more common interests who interact frequently on social networking services,a novel algorithm for overlapping community detection in a heterogeneous network containing both directed and undirected edges is proposed.This algorithm involves seed selection and community initialization and expansion to effectively and efficiently unfold modules in parallel.The experimental results obtained using synthetic and real-world social networks illustrate the higher accuracy and lower time consumption of the proposed approach than those of the existing state-of-the-art methods.(2)For adapting functionality to personal users,some applications require the interest prediction information of these persons.To effectively predict a user's interests,in this paper,we present a hybrid user modeling framework that integrates isolated interest extraction via text analysis into a social-relationship-based method using community detection.The experimental results obtained using a large-scale micro-blogging dataset with 12,746 users illustrate that our hybrid scheme for socialized user modeling can obviously improve the prediction accuracy of the user's interests,in comparison with the text-based approach.(3)A considerably large number of online users and their diverse activities have posed great challenges on recommendation systems.However,most of the existing frameworks for recommending friends cannot simultaneously meet the requirements of accuracy and timeliness.By taking full advantage of latent Dirichlet allocation(LDA),in this paper,we propose a friend recommendation scheme combining interaction-based topologies and interest-based features with linear computational complexity.The experimental results obtained in a real-world micro-blogging scenario display that our hybrid approach outperforms the other three methods in terms of effectiveness and efficiency.(4)Influence maximization is an issue that involves finding a small set of highly influential individuals who lead to a faster and wider spread in a social network for understanding and controlling the information and behavior dissemination.A novel scheme for identifying a pre-fixed number of influential members in micro-blogging platforms is proposed to maximize the impact;it combines the social interactions and the interest similarities between users by using graph partitioning.The experimental results obtained using synthetic and real-world micro-blogging networks demonstrate that the proposed approach outperforms the other state-of-the-art algorithms in terms of effectiveness and efficiency.