This article explores festival attendees’ experiences on social media. Data on festival attendees’ posts on the Guangzhou International Light Festival were collected from TikTok. The Jieba Chinese word segmentation tool was applied to tokenize the texts and then classify the high-frequency words and phrases into five dimensions of festival experiences. The five dimensions of festival attendees’ experiences are cognitive, novel, affective, physical participation, and social interaction. A combination of the modularity algorithm and Gephi was used to cluster and visualize the festival attendees’ overall experiences and the different experiences in terms of gender and age. The results indicate that, in festival attendees’ overall experience, cognitive experience accounted for the largest proportion, followed by physical participation, and the proportion of social interaction, emotional experience, and novel experience was less. Moreover, the demographic characteristics of gender and age differ in the festival experience.
Recently, hybrid-order community detection has been proposed for addressing the hypergraph fragmentation issue suffered by the motif-based higher-order community detection. However, the existing attempts of hybrid-order community detection inadvertently damage the lower-order connectivity pattern and the higher-order connectivity pattern when constructing the fusion model. Additionally, like the higher-order community detection approaches, they also adopt a two-phase strategy that separately applies the existing graph node clustering methods to the proximity matrix derived from the lower-order connectivity pattern and the higher-order connectivity pattern. Therefore, the higher-order connectivity pattern is only utilized for constructing proximity matrix and hence has no direct effect on the final community results. In this paper, to address the above issues, we propose a Generative model for Hybrid-Order Community detection (GHOC). The main idea lies in defining a likelihood function of a generative model that finds the optimal community membership strength vectors of nodes, based on which the original lower-order connectivity pattern and the higher-order connectivity pattern can be directly reconstructed simultaneously. From the community membership strength vectors, the final community structure can be derived. Extensive
Background: Experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identify drug–target interactions (DTIs) has been research focus. In present, many machine learning algorithms are used for predicting drug-target interactions. The key idea is to train the classifier using existing DTI so as to predict new or unknown DTI. However, there are various challenges such as class imbalance and parameter optimization of many classifiers that need to be solved before developing an optimal drug-target interaction model. Methods: In this paper, we propose a framework named SSELM-neg for drug-target interaction prediction, in which a screening approach was used to choose high quality negative samples and a spherical search approach is used to optimize the parameters of the extreme learning machine. Results: Results show that the performance of the proposed technique outperforms the other state-of-the-art methods in 10-fold cross-validation experiments in terms of AUC(0.986, 0.993, 0.988, 0.969 ) and AUPR(0.982, 0.991, 0.982, 0.946) for E dataset, GPCR dataset, IC dataset and NR dataset. Conclusion: Screening approach produce the high quality negative samples with the same numbers of the positive samples, which solve the class imbalance problem. Extreme learning machine was optimized by a spherical search approach in identifying DTIs. Therefore, our models achieve better performance than other state-of-the-art methods.
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