Community detection is a fundamental problem in network analysis for identifying densely connected node clusters, with successful applications in diverse fields like social networks, recommendation systems, biology, and cyberattack detection. Overlapping community detection refers to the case of a node belonging to multiple communities simultaneously, which is a much more meaningful and challenging task. Graph representation learning with Evolutionary Computation has been studied well in overlapping community detection to deal with complex network structures and characteristics. However, most of them focus on searching the entire solution space, which can be inefficient and lead to inadequate results. To overcome the problem, a structural feature node extraction method is first proposed that can effectively map a network into a structural embedding space. Thus, nodes within the network are classified into hierarchical levels based on their structural feature strength, and only nodes with relatively high strength are considered in subsequent search steps to reduce the search space. Then, a maximal-clique representation method is employed on the given vertex set to identify overlapping nodes. A hybrid clique-based multi-objective evolutionary algorithm with decomposition method is designed to address cliques and the remaining unexplored nodes separately. The number of communities generated with this allocation method is closer to the actual partition count with high division quality. Experimental results on nine usually used real-world networks, five synthetic networks, and two large-scale networks demonstrate the effectiveness of the proposed methodology in terms of community quality and algorithmic efficiency, compared to traditional, MOEA-based, and graph embedding-based community detection algorithms.

High-utility itemset mining (HUIM) can consider not only the profit factor but also the profitable factor, which is an essential task in data mining. However, most HUIM algorithms are mainly developed on a single machine, which is inefficient for big data since limited memory and processing capacities are available. A parallel efficient high-utility itemset mining (P-EFIM) algorithm is proposed based on the Hadoop platform to solve this problem in this paper. In P-EFIM, the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce framework. Then the ordered itemsets are renumbered, and the low-utility itemsets are pruned to improve the dataset utility. In the Map phase, the P-EFIM algorithm divides the task into multiple independent subtasks. It uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure load-balancing. Furthermore, the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce phase. Experiments are performed on eight datasets, and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth, which is also HUIM algorithm based on the Hadoop framework.