Influence maximization (IM) is fundamental to social network applications. It aims to find multiple seed nodes with an enormous impact cascade to maximize these nodes’ spread of influence in social networks. Traditional methods for solving influence maximization of the social network, such as the distance method, greedy method, and PageRank method, may suffer from issues of low calculation accuracy and high computational cost. In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. In this algorithm, a novel grid-based reproduction strategy and a direction-adjustment-based chemotaxis strategy are devised to enhance the algorithm’s searchability. Finally, we conduct comprehensive experiments on four social network cases to verify the effectiveness of the proposed algorithm. The experimental results show that our proposed algorithm effectively solves the social network’s influence maximization.

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With the development of electric power industries, the number of standards has grown rapidly. However, the contents of standard clauses extracted from various fields are often inconsistent. It is difficult for the staff to choose the proper standard clauses in their work. Therefore, it is significant to provide staff with consistent electric power standard clauses. This paper takes the standard documents in the electric power field as the data source, and focuses on how to find out the related but inconstant clauses in the documents. We take advantage of the entity relationships of knowledge graph to get the discrepancies of electric power standard clauses. The experimental results illustrate the good performance of the proposed method in terms of precision and recall. The precision rate is 76.45%, and the recall rate reaches 84.72%. In addition, the proposed approach could also provide a solution to the differential discrimination of standard documents in various industries.

The standard clauses used in the electric power industry often contain inconsistencies, making it difficult for professionals to select appropriate terms in their work. In this paper, we propose a discrepancy discrimination model based on knowledge graph and natural language processing, using standard clauses of power equipment as the dataset. Furthermore, we construct an ensemble learning model to assist decision-making in the electric power field. The experimental results show that the proposed method is effective in terms of precision and recall. The discrepancy discrimination model has a precision rate of 76.45% and recall rate of 84.72%, while the auxiliary decision-making model has a precision rate of 75.3%. The method is able to effectively identify inconsistencies in equipment standards and provide decision-making advice, providing a valuable reference for standardizing the content of standard terms in the industry.