A New Classification Method of Signature Network Node Based on Potential Space Projection Social network node classification is widely used in solving practical problems. Most of the existing network node classification algorithms are concentrated on the unsigned social network, or the network containing only positive links. Rare works study the node classification on signed social networks, where each link is assigned a positive or negative sign. Recently, the research of the signed network has proved that the negative link has more effect on the signed network analysis than the positive links. Therefore, negative links play a key role in many tasks of signed network analysis. In this paper, we study the classification of nodes in the signed network. We project the positive and negative network to the corresponding latent space, and propose a mathematical model based on latent space. An iterative algorithm is proposed to optimize the model, and the iterative optimization of latent space matrix and projection matrix is used to classify the nodes in the network. The experimental results of the data set of the signed social network show that the classification results of the algorithm have high accuracy.

Network2Vec: Learning Node Representation Based on Space Mapping in Networks Complex networks represented as node adjacency matrices constrains the application of machine learning and parallel algorithms. To address this limitation, network embedding (i.e., graph representation) has been intensively studied to learn a fixedlength vector for each node in an embedding space, where the node properties in the original graph are preserved. Existing methods mainly focus on learning embedding vectors to preserve nodes proximity, i.e., nodes next to each other in the graph space should also be closed in the embedding space, but do not enforce algebraic statistical properties to be shared between the embedding space and graph space. In this work, we propose a lightweight model, entitled Network2Vec, to learn network embedding on the base of semantic distance mapping between the graph space and embedding space. The model builds a bridge between the two spaces leveraging the property of group homomorphism. Experiments on different learning tasks, including node classification, link prediction, and community visualization, demonstrate the effectiveness and efficiency of the new embedding method, which improves the stateoftheart model by 19% in node classification and 7% in link prediction tasks at most. In addition, our method is significantly faster, consuming only a fraction of the time used by some famous methods.