Unattributed social networks are more complicated, and it tends not to determine the best division by over-optimizing a theoretical measure for unsupervised algorithms. Nowadays, communities strongly overlap due to the fact that people strongly interact, which makes community detection even more challenging. The paper develops a new algorithm by rearranging ‘indivisible’ blocks (RaidB). In RaidB, we first initialize ‘indivisible’ blocks by disjoint k-clique blocks in a network, and then these blocks are rearranged by moving nodes from one block to another based on maximizing modularity to uncover non-overlapping communities. For identifying overlapping communities, the above blocks are further rearranged, i.e., each block is subdivided and expanded to determine sub-blocks by introducing a dynamic linear threshold (DLT) model for influence interpenetration, and we finally determine a division from these sub-blocks with the minimum size that can cover the network. We compare RaidB with the existing state of the art methods for non-overlapping and overlapping community detection. The results show that RaidB tends to achieve better performance especially on sparse networks with unobvious communities and networks with strongly overlapping communities.

The proposed new fusion algorithm is based on the finite ridgelet transform(FRIT) and PCA. FRIT could capture two and higher dimensional singularity is analyzed. FRIT is used to decompose the image into low and high frequency components. The PCA method is used to fuse the low frequency coefficients. And for the high frequency coefficients, the maximum-method and the region consistency check are adopted. Experiments show that the proposed algorithm outperforms the wavelet transform method and the Laplacian pyramid methods in preserving the edge and texture information.