Titlebook: Computational Data and Social Networks; 11th International C Thang N. Dinh,Minming Li Conference proceedings 2023 The Editor(s) (if applica

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书目名称Computational Data and Social Networks影响因子(影响力)




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书目名称Computational Data and Social Networks网络公开度




书目名称Computational Data and Social Networks网络公开度学科排名




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书目名称Computational Data and Social Networks被引频次学科排名




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Albinism

狂热语言

OREX

We will fight them on the Beachesstance for group recommendation system. The proposed recommendation model is evaluated on the Jester5k and the MovieLens datasets. The experiment result shows the feasibility of applying the potential energy for the group recommendation problems.

语源学

Carl Friedrich Graumann,Margret Wintermantelrotocol. An evaluation of the implementation is also conducted. Experimental results show that the cost of transactions decreases depending on the batch size, with the gas cost decreasing by more than 85% for a batch size of 50 transactions. Other evaluation results reveal that deposits incur the most cost and increase faster with the batch size.

迫击炮

0302-9743 held as a Virtual Event, during December 5–7, 2022. The 17 full papers and 7 short papers included in this book were carefully reviewed and selected from 47 submissions. They were organized in topical sections as follows: Machine Learning and Prediction, Security and Blockchain, Fact-checking, Fake

迫击炮

投票

We will fight them on the Beachesand the word alignment process’s performance, so the proposed strategy can be extended and applied to another low-resource language as long as there is a large bilingual corpus with a rich resource language.

Crumple

https://doi.org/10.1007/978-1-4612-3582-8 Results show that the degree-based attack on the global component is more effective than the classical attack on the entire network. In contrast, the classical Betweenness attack slightly outperforms the Betweenness attack on the global component. However, the latter is more efficient.

不法行为

Incorporating Neighborhood Information and Sentence Embedding Similarity into a Repost Prediction Mothe-art machine learning methods, e.g., Logistic Regression, K-nearest Neighbors, Gaussian Naive Bayes, Deep Neural Network, Random Forest, XGBoosting and Stacking Model to predict repost probability. We evaluate our model on real dataset Weibo to compare the performance with different features and machine learning methods.