The Research Of Community Analysing Based On Signed Social Networks

With the development of information technology, applications of social network analysis are not limited to the traditional social relations of small group, but extend to almost all areas of human activities, covering sociology, economics, politics, information science, and many other directions .However, currently the social network models adopt directed edges or undirected edges to denote the relationship between network nodes, while the prevailing social networks generated by the antagonistic relationship have not discussed in depth with signed edges. Therefore, it is necessary to provide more effective methods for the signed social network.The community analysis methods of signed network mainly discuss from the two aspects, the partition of community structure and the link prediction of network. The community structure partition of signed network is developed from the balance theory of social relations. Through the combination of K-core partition and optimizating objective function to complete the community structure partition of signed network, comparing with the random partition, it improves the final results. At the same time, based on common neighbor algorithm together with the preferential attachment theory, we propose a improved link sign prediction method of signed network, which effectively avoids the sensitivity of the selected value on the single common neighbor algorithm , so that it can predict the sign of unknown side and absence edge in the network more accurately.We verify the community partition strategy and link sign prediction of signed network by the simulation of signed network data sets. The results indicate that there are some improvements in the evaluation of the above two methods in terms of the property and those methods can be better applied in real signed network data sets...