Research And Implementation Of Network Community Detection Methods Based On Link Density

With the development of internet and related information techniques, the humansociety is entering the age of networks. Complex networks are everywhere around us.One of the most important properties of the complex network is the internalcommunities of it. Mining the community structure of the complex network can help usreveal the topology structure of networks, analyze their function and hidden pattern, andpredict the action of complex network, which are valuable in search engine,recommender system, Precision marketing and so on.In this thesis, we investigate the definition, structure and the mainstream method ofnetwork community detection at first. Based on this, a local community quality criterionis proposed as well as a fast online multi-resolution overlapping community detectionalgorithm. The algorithm proposed does not need any global information of the networkand user intervention, can identify local community from a given start vertex. Then,another new community identifying method based on the skeleton tree of the network isproposed by combination of density based clustering method and minimum spanningtree based clustering method. The proposed method can identify any size of communityas well as hubs and outliers, and locate the optimal density threshold.At last, detail material experiment is made on some real world and manmadenetworks. Results show that methods proposed in this thesis are accurate and fast, canprocess networks with any scale and shape.