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2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)最新文献

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Predicting the evolution of communities in social networks using structural and temporal features 利用结构和时间特征预测社会网络中社区的演变
Maria Evangelia G. Pavlopoulou, Grigorios Tzortzis, D. Vogiatzis, G. Paliouras
During the last years, there is increasing interest in analyzing social networks and modeling their dynamics at different scales. This work focuses on predicting the future form of communities, which represent the mesoscale structure of networks, while the communities arise as a result of user interaction. We employ several structural and temporal features to represent communities, along with their past form, that are used to formulate a supervised learning task to predict whether a community will continue as currently is, shrink, grow or completely disappear. To test our methodology, we created a real-life social network dataset consisting of an excerpt of posts from the Mathematics Stack Exchange Q&A site. In the experiments, special care is taken in handling the class imbalance in the dataset and in investigating how the past evolutions of a community affect predictions.
在过去的几年里,人们对分析社会网络和在不同尺度上对其动态建模越来越感兴趣。这项工作的重点是预测社区的未来形式,它代表了网络的中尺度结构,而社区则是用户交互的结果。我们使用几个结构和时间特征来代表社区,以及它们过去的形式,这些特征用于制定监督学习任务,以预测社区是否会像现在一样继续,缩小,增长或完全消失。为了测试我们的方法,我们创建了一个真实的社交网络数据集,该数据集由数学堆栈交换问答网站上的帖子摘录组成。在实验中,特别注意处理数据集中的类不平衡,并调查社区过去的演变如何影响预测。
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引用次数: 31
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2017 12th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)
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