Influence maximization in a social network refers to the selection of node sets that support the fastest and broadest propagation of information under a chosen transmission model. The efficient identification of such influence-maximizing groups is an active area of research with diverse practical relevance. Greedy-based methods can provide solutions of reliable accuracy, but the computational cost of the required Monte Carlo simulations renders them infeasible for large networks. Meanwhile, although network structure-based centrality methods can be efficient, they typically achieve poor recognition accuracy. Here, we establish an effective influence assessment model based both on the total valuation and variance in valuation of neighbor nodes, motivated by the possibility of unreliable communication channels. We then develop a discrete moth-flame optimization method to search for influence-maximizing node sets, using a local crossover and mutation evolution scheme atop the canonical moth position updates. To accelerate convergence, a search area selection scheme derived from a degree-based heuristic is used. The experimental results on five real-world social networks, comparing our proposed method against several alternatives in the current literature, indicates our approach to be effective and robust in tackling the influence maximization problem.

Rumor spreading can have a significant impact on people’s lives, distorting scientific facts and influencing political opinions. With technologies that have democratized the production and reproduction of information, the rate at which misinformation can spread has increased significantly, leading many to describe contemporary times as a ‘post-truth era’. Research into rumor spreading has primarily been based on either model of social and biological contagion, or upon models of opinion dynamics. Here we present a comprehensive model that is based on information entropy, which allows for the incorporation of considerations like the role of memory, conformity effects, differences in the subjective propensity to produce distortions, and variations in the degree of trust that people place in each other. Variations in the degree of trust are controlled by a confidence factor β, while the propensity to produce distortions is controlled by a conservation factor K. Simulations were performed using a Barabási–Albert (BA) scale-free network seeded with a single piece of information. The influence of β and K upon the temporal evolution of the system was subsequently analyzed regarding average information entropy, opinion fragmentation, and the range of rumor spread. These results can aid in decision-making to limit the spread of rumors.

Electroencephalography (EEG) is a complex bioelectrical signal. Analysis of which can provide researchers with useful physiological information. In order to recognize and classify EEG signals, a pattern recognition method for optimizing the support vector machine (SVM) by using improved squirrel search algorithm (ISSA) is proposed. The EEG signal is preprocessed, with its time domain features being extracted and directed to the SVM as feature vectors for classification and identification. In this paper, the method of good point set is used to initialize the population position, chaos and reverse learning mechanism are introduced into the algorithm. The performance test of the improved squirrel algorithm (ISSA) is carried out by using the benchmark function. As can be seen from the statistical analysis of the results, the exploration ability and convergence speed of the algorithm are improved. This is then used to optimize SVM parameters. ISSA-SVM model is established and built for classification of EEG signals, compared with other common SVM parameter optimization models. For data sets, the average classification accuracy of this method is 85.9%. This result is an improvement of 2–5% over the comparison method.