Social networks that are dynamic contain rich network structure and content information. In dynamic networks, it is necessary to discover communities and their topical meanings. However, existing methods either only discover communities with ignoring their topical meaning in dynamic networks, or they discover communities and their topics in static networks. In this paper, we identify the problem of dynamic topical community detection and propose a dynamic topical community detection (DTCD) method to detect communities and their topical meanings in dynamic networks. The DTCD is a generative model integrating network structure, text, and time. The DTCD considers a community as a mixture of topics and generates the neighbors and documents of the node and their time stamps at the same time via the community. The latent variables are learned by collapsed Gibbs sampling. The DTCD not only can find communities and their topics, but also capture the temporal variations of communities and topics. The experimental results on two real-world datasets demonstrate the effectiveness of DTCD. INDEX TERMS Social network, dynamic community detection, user generated content, generative model, collapsed Gibbs sampling.
Multi-agent multi-target search strategies can be utilized in complex scenarios such as post-disaster search and rescue by unmanned aerial vehicles. To solve the problem of fixed target and trajectory, the current multi-agent multi-target search strategies are mainly based on deep reinforcement learning (DRL). However, the training of agents by the DRL tend to be brittle due to their sensitivity to the training environment, which makes the strategies learned by the agents fall into local optima frequently, resulting in poor system robustness. Additionally, sparse rewards in DRL will lead to the problems such as difficulty in system convergence and low utilization efficiency of the sampled data. To address the problem that the robustness of the agents is weakened and the sparse rewards exist in the multi-objective search environment, we propose a MiniMax Multi-agent Deep Deterministic Policy Gradient based on the Parallel Hindsight Experience Replay (PHER-M3DDPG) algorithm, which adopts the framework of centralized training and decentralized execution in continuous action space. To enhance the system robustness, the PHER-M3DDPG algorithm employs a minimax learning architecture, which adaptively adjusts the learning strategy of agents by involving adversarial disturbances. In addition, to solve the sparse rewards problem, the PHER-M3DDPG algorithm adopts a parallel hindsight experience replay mechanism to increase the efficiency of data utilization by involving virtual learning targets and batch processing of the sampled data. Simulation results show that the PHER-M3DDPG algorithm outperforms the existing algorithms in terms of convergence speed and the task completion time in a multi-target search environment.
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