Concerning the problem that community detection algorithm based on label propagation in complex networks has a pre-parameter limit in the real network and redundant labels, a community detection model in large scale academic social networks was proposed. The model detected Utmost Maximal Cliques (UMC) in the academic social network and arbitrary intersection between the UMC is the empty set, and then let nodes of each UMC share the unique label by reducing redundant labels and random factors, so the model increased the efficiency and stability of the algorithm. Meanwhile the model completed label propagation of the UMC adjacent nodes using closeness from core node groups (UMC) to spread around, Non-UMC adjacent nodes in the network were updated according to the maximum weight of its neighbor nodes. In the post-processing stage an adaptive threshold method removed useless labels, thereby effectively overcame the pre-parameter limitations in the real complex network. The experimental results on academic social networking platform-SCHOLAT data set prove that the model has an ability to assign nodes with certain generality to the same community, and it provides support of the academic social networks precise personalized service in the future, such as latent friend recommendation and paper sharing.

For resolving the problem of curvature calculation for scattered point cloud data with strong noise, a robust statistics approach to curvature estimation was presented. Firstly the local shape at a sample point in 3D space was fitted by a quadratic surface. In addition,the fitting was performed at multiple times with randomly sampled subsets of points, and the best fitting result evaluated by variable-bandwidth maximum kernel density estimator was obtained. At last, the sample point was projected onto the best fitted surface and the curvatures of the projected point was estimated. The experimental results demonstrate that the proposed method is robust to noise and outliers. Especially with increasing noise variance, the proposed method is significantly better than the traditional parabolic fitting method.