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.