The node embedding method enables network structure feature learning and representation for social network community detection. However, the traditional node embedding method only focuses on a node’s individual feature representation and ignores the global topological feature representation of the network. Traditional community detection methods cannot use the static node vector from the traditional node embedding method to calculate the dynamic features of the topological structure. In this study, an incremental dynamic community detection model based on a graph neural network node embedding representation is proposed, comprising the following aspects. A node embedding model based on influence random walk improves the information enrichment of the node feature vector representation, which improves the performance of the initial static community detection, whose results are used as the original structure of dynamic community detection. By combining a cohesion coefficient and ordinary modularity, a new modularity calculation method is proposed that uses an incremental training method to obtain node vector representation to detect a dynamic community from the perspectives of coarse- and fine-grained adjustments. A performance analysis based on two dynamic network datasets shows that the proposed method performs better than benchmark algorithms based on time complexity, community detection accuracy, and other indicators.
Exercise recommendation is an integral part of enabling personalized learning. Giving appropriate exercises can facilitate learning for learners. The programming problem recommendation is a specific application of the exercise recommendation. Therefore, an innovative recommendation framework for programming problems that integrate learners’ learning styles is proposed. In addition, there are some difficulties to be solved in this framework, such as quantifying learning behavior, representing programming problems, and quantifying learning strategies. For the difficulties in quantifying learning behavior and quantifying learning strategies, a programming problem recommendation algorithm based on deep reinforcement learning (DRLP) is proposed. DRLP includes the specific design of action space, action-value Q-network, and reward function. Learning style is embedded into DRLP through action space to make recommendations more personalized. To represent the programming problem in DRLP, a multi-dimensional integrated programming problem representation model is proposed to quantify the difficulty feature, knowledge point feature, text description, input description, and output description of programming problems. In particular, Bi-GRU is introduced to learn texts’ contextual semantic association information from both positive and negative directions. Finally, a simulation experiment is carried out with the actual learning behavior data of 47,147 learners in the LUOGU Online Judge system. Compared with the optimal baseline model, the recommendation effect of DRLP has improved (HR, MRR, and Novelty have increased by 4.35%, 1.15%, and 1.1%), which proves the rationality of the programming problem representation model and action-value Q-network.
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