Trinh Thanh, Wu Dingming, Huang Joshua Zhexue, Azhar Muhammad
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
Entropy (Basel). 2020 Jan 18;22(1):119. doi: 10.3390/e22010119.
Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three well-known machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features.
基于事件的社交网络（EBSN）被广泛用于为用户创建在线社交群组并组织线下活动。活跃度和忠诚度是这些在线社交群组的关键特征，对于确定特定时间段内社交群组的发展或停滞至关重要。然而，针对这些概念的研究较少，难以阐明基于事件的社交网络中群组的存在情况。在本文中，我们研究群组活跃度和用户忠诚度问题，以提供对在线社交网络的全新见解。首先，我们分析EBSN的结构并从爬取的数据集中生成特征。其次，我们基于一系列时间窗口定义群组活跃度和用户忠诚度的概念，并提出一种测量群组活跃度的方法。在所提出的方法中，我们首先计算两个连续时间窗口之间的事件数量比率。然后，我们开发一个关联矩阵，在几个连续时间窗口后为每个群组分配活跃度标签。同样，我们根据时间窗口中收集的参与事件来衡量用户忠诚度，并将忠诚度视为群组活跃度的一个贡献特征。最后，使用三种著名的机器学习技术来验证活跃度标签并为每个群组生成特征。结果，我们还发现一小部分特征高度相关，与整个特征相比能带来更高的准确率。
