Research On Adaptive Community Detection Algorithms In Social Networks

There is a phenomenon of uneven relationship in the social network,forming the community structure.The community structure in the network helps to simplify the analysis of network topology structure,reveal the internal rules of the system,and provide powerful support for information recommendation and information dissemination control.Therefore,the community detection algorithm is an important part of network analysis,which can simplify the network structure analysis.The community detection algorithm has been widely used in network analysis and how to detect community structure adaptively without parameters is always a hot research topic.In this paper,an adaptive community detection algorithm based on information transfer and peak clustering is proposed.The trust degree function between nodes and neighbors is defined,and each node independently spreads the amount of information to the network based on the trust degree.After the diffusion,the total information amount of the node is the density of the density peaks.The distance between the nodes in the network is replaced by the reciprocal of the information amount of the destination node.Then,a method that can automatically select core nodes is proposed and the core nodes are divided into different communities,and the remaining nodes are allocated to the community of the closest core node.The algorithm has the advantage that no additional parameters are needed and the internal structure of the community can be found.The experimental results verify the feasibility and effectiveness of the algorithm.Then,on the basis of the first algorithm,an overlapping community detection algorithm based on the core node information is proposed.Using the information value of core nodes contained in non-core nodes and the information scaling factor,the community of non-core nodes is judged and then complete the network overlapping community detection.The experimental results show that the algorithm is superior to CMP and COPRA algorithms.Finally,using edge clustering,we propose an adaptive overlapping community detection algorithm based on density peak edge clustering and information transmission.The function of trust between edges is defined and the transmission of information between edges is used to determine the core edge.Then,the different communities are allocated for the core edge,and the remaining edge is assigned to the community where the core edge that has the shortest distance to it is located.Finally the overlapping community is obtained from the edge community.The experimental results show that the algorithm is superior to CMP and COPRA algorithms,and the advantage is more obvious in the high overlapping data.