Aiming at the fact that the existing video person re-identification methods cannot effectively extract the spatiotemporal information between consecutive frames of the video, a person re-identification network based on non-local attention and multi-feature fusion was proposed to extract global and local representation features and time series information. Firstly, the non-local attention module was embedded to extract global features. Then, the multi-feature fusion was realized by extracting the low-level and middle-level features as well as the local features, so as to obtain the salient features of the person. Finally, the similarity measurement and sorting were performed to the person features in order to calculate the accuracy of video person re-identification. The proposed model has significantly improved performance compared to the existing Multi-scale 3D Convolution (M3D) and Learned Clip Similarity Aggregation (LCSA) models with the mean Average Precision (mAP) reached 81.4% and 93.4% respectively and the Rank-1 reached 88.7% and 95.3% respectively on the large datasets MARS and DukeMTMC-VideoReID. At the same time, the proposed model has the Rank-1 reached 94.8% on the small dataset PRID2011.

To solve the problem of commodity recommendation based on network information, a recommendation algorithm based on community mining and label propagation on bipartite network was proposed. Firstly, a weighted bipartite graph was used to represent the user-item scoring matrix, and the label propagation technology was adopted to perform the community mining to the bipartite network. Then, the items which the users might be interested in were mined based on the community structure information of the bipartite network and by making full use of the similarity between the communities that the users in as well as the similarity between items and the similarity between the users. Finally, the item recommendation was performed to the users. The experimental results on real world networks show that, compared with the Collaborative Filtering recommendation algorithm based on item rating prediction using Bidirectional Association Rules (BAR-CF), the Collaborative Filtering recommendation algorithm based on Item Rating prediction (IR-CF), user Preferences prediction method based on network Link Prediction (PLP) and Modified User-based Collaborative Filtering (MU-CF), the proposed algorithm has the Mean Absolute Error (MAE) 0.1 to 0.3 lower, and the precision 0.2 higher. Therefore, the proposed algorithm can obtain recommendation results with higher quality compared to other similar methods.

Social network node classification is widely used in solving practical problems. Most of the existing network node classification algorithms focus on unsigned social networks,while node classification algorithms on social networks with symbols on edges are rare. Based on the fact that the negative links contribute more on signed network analysis than the positive links. The classification of nodes on signed networks was studied. Firstly, positive and negative networks were projected to the corresponding latent spaces, and a mathematical model was proposed based on positive and negative links in the latent spaces. Then, an iterative algorithm was proposed to optimize the model, and the iterative optimization of latent space matrix and projection matrix was used to classify the nodes in the network. The experimental results on the dataset of the signed social network show that the F1 value of the classification results by the proposed algorithm is higher than 11 on Epinions dataset, and that is higher than 23.8 on Slashdo dataset,which indicate that the proposed algorithm has higher accuracy than random algorithm.

Several problems exist in trawling algorithm, such as too many Web communities, high repetition rate between community-cores and isolated community formed by strict definition of community. Thus, an algorithm detecting Blog community based on Formal Concept Analysis (FCA) was proposed. Firstly, concept lattice was formed according to the linkage relations between Blogs，then clusters were divided from the lattice based on equivalence relation, finally communities were clustered in each cluster based on the similarity of concepts. The experimental results show that, the community-cores, which network density is greater than 40%, occupied 83.420% of all in testing data set, the network diameter of combined community is 3, and the content of community gets enriched significantly. The proposed algorithm can be effectively used to detect communities in Blog, micro-Blog and other social networks, and it has significant application value and practical meaning.

An adaptive multiple colony ant algorithm was presented to solve frenquency assignment problem of mobile communicaiton. Unlike the traditional ant colony algorithm which used only one ant colony, our algorithm used multiple ant colonies. For each ant colony, a coefficient of convergence was defined by which the ants adaptively could choose the path, update their local pheromone and exchange information between colonies. By using the adaptive strategy to update the pheromone, the balance between the diversity and convergence of every ant colony was kept. The simulation results on the fixed frequency assignment problem and minimal span frequency assignment problem show that our algorithm has global convergence and higher speed of optimization.