To fully characterize the evolution process of the topological structure of dynamic communities, we propose a dynamic community detection based on Evolutionary DeepWalk (DEDW) for the high-dimensional data and dynamic characteristics. First, DEDW solves the problem of data sparseness in the process of dynamic network data representation through graph embedding. Then, DEDW uses the DeepWalk algorithm to generate node embedding feature vectors based on the characteristics of the stable change of the community structure; finally, DEDW integrates historical network structure information to generate evolutionary graph features and implements dynamic community detection with the K-means algorithm. Experiments show that DEDW can mine the time-smooth change characteristics of dynamic communities, solve the problem of data sparseness in the process of node embedding, fully consider historical structure information, and improve the accuracy and stability of dynamic community detection.

Accurate localization in underground coal mining is a challenging technology in coal mine safety production. This paper proposes a low-cost battery-free localization scheme based on depth images, called MineBL. The main idea is to utilize the battery-free low-cost reflective balls as position nodes and realize underground target localization with a series of algorithms. In particular, the paper designs a data enhancement strategy based on small-target reorganization to increase the identification accuracy of tiny position nodes. Moreover, a novel ranging algorithm based on multi-filter cooperative denoising has been proposed, and an optimized weighted centroid location algorithm based on multilateral location errors has been designed to minimize underground localization errors. Many experiments in the indoor laboratories and the underground coal mine laboratories have been conducted, and the experimental results have verified that MineBL has good localization performances, with localization errors less than 30 cm in 95% of cases. Therefore, MineBL has great potential to provide a low-cost and effective solution for precise target localization in complex underground environments.

Personalized, diversified and decentralized mobile social networking platforms provide users with an environment for accessing, sharing, distributing information and communicating emotionally. In this virtual social space, effective identification of trusted nodes can assist social networking users to make interactive strategies, and provide enterprises with scientific basis for the social interactive marketing decisions. Therefore, trust assessment plays a crucial role in establishing social connections among mobile social networking users. In this paper, based on the social relationship and information interactive data between the users of Sina Weibo, four factors, the intensity of social relations, the sphere of social influence, the information value and the control of information transmission are taken into account to build the trust assessment model. Secondly, the trust between users is quantified by node credibility and entropy weight method is employed to calculate the four factors' contribution to the node credibility respectively. Finally, the credibility of each node in Sina Weibo is calculated to identify the trusted nodes.