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Titlebook: Computational Data and Social Networks; 9th International Co Sriram Chellappan,Kim-Kwang Raymond Choo,NhatHai P Conference proceedings 2020

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發(fā)表于 2025-3-26 23:42:28 | 只看該作者
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發(fā)表于 2025-3-27 03:51:15 | 只看該作者
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An Analysis of Users Engagement on Twitter During the COVID-19 Pandemic: Topical Trends and Sentimenstate-of-the-art deep learning techniques, the trending topics within the tweets on monthly-bases, including their sentiment and user’s perception, were analyzed. This study highlights the change of the public behavior and concerns during the pandemic. Users expressed their concerns on health servic
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發(fā)表于 2025-3-27 20:04:48 | 只看該作者
Group Influence Maximization in Social Networksstly, experiments are carried out to demonstrate that our CMC and IRIS both outperform the known baselines including Maximum Coverage and Maximum Out-degree algorithms in the average number of activated groups under IC model.
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發(fā)表于 2025-3-27 21:55:37 | 只看該作者
Maximum Channel Access Probability Based on Post-Disaster Ground Terminal Distribution Densityred into a convex problem by analyzing objective function. An interior-point method is adopted to solve this problem. Extensive simulations are performed to evaluate the model by optimizing the UAV speed in different regions. The results show that the proposed new scheme outperforms the one with con
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發(fā)表于 2025-3-28 05:08:48 | 只看該作者
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發(fā)表于 2025-3-28 09:17:55 | 只看該作者
Sriram Chellappan,Kim-Kwang Raymond Choo,NhatHai P
40#
發(fā)表于 2025-3-28 14:09:53 | 只看該作者
https://doi.org/10.1007/978-3-030-75622-2between users in social. Our model supplies the principle of organizing interactions as a graph, combines information from social network and all kind of relations in the heterogeneous knowledge graph. The model is evaluated on real world datasets to demonstrate this method’s effectiveness.
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