具有外生交互网络的在线社交网络中的链接分类和关系强度排序
Link Classification and Tie Strength Ranking in Online Social Networks with Exogenous Interaction Networks
摘要：
在线社交网络（OSN）已经成为连接人们、共享知识和信息以及进行通信的主要媒介。使用这些OSN的人之间的社交联系形成为虚拟链接（例如，友谊和后续连接）连接人。这些链接是当今OSN的核心，因为它们促进了社交网络成员可以进行的所有活动。然而，这些网络中的许多遭受噪声链路，即，不反映真实关系的链接或强度较低的链接，这些链接改变了网络的结构，并妨碍了对这些网络的准确分析。因此，为了维持一个健康和真实的网络，对社交网络中的链接进行评估和排名的过程至关重要。在这里，我们将链路评估定义为识别网络中有噪声和无噪声链路的过程。在本文中，我们解决的问题，链接评估和链接排名的社交网络使用外部交互网络。除了友谊社交网络之外，还利用额外的外源交互网络来使评估过程更有意义。我们采用了机器学习分类器，使用来自外生交互网络的数据对感兴趣的社交网络中的链接进行评估和排名。该方法用两个不同的数据集进行了测试，每个数据集都包含感兴趣的社交网络，具有地面真实性，以及外源交互网络。结果表明，仅利用外生交互网络中单个网络的结构，以及利用外生交互网络的整体结构，都可以有效地评估社会网络的链接。实验表明，一些分类器做得比别人更好，无论是链接分类和链接排名。
英文摘要：
Online social networks (OSNs) have become the main medium for connecting people, sharing knowledge and information, and for communication. The social connections between people using these OSNs are formed as virtual links (e.g., friendship and following connections) that connect people. These links are the heart of today's OSNs as they facilitate all of the activities that the members of a social network can do. However, many of these networks suffer from noisy links, i.e., links that do not reflect a real relationship or links that have a low intensity, that change the structure of the network and prevent accurate analysis of these networks. Hence, a process for assessing and ranking the links in a social network is crucial in order to sustain a healthy and real network. Here, we define link assessment as the process of identifying noisy and non-noisy links in a network. In this paper, we address the problem of link assessment and link ranking in social networks using external interaction networks. In addition to a friendship social network, additional exogenous interaction networks are utilized to make the assessment process more meaningful. We employed machine learning classifiers for assessing and ranking the links in the social network of interest using the data from exogenous interaction networks. The method was tested with two different datasets, each containing the social network of interest, with the ground truth, along with the exogenous interaction networks. The results show that it is possible to effectively assess the links of a social network using only the structure of a single network of the exogenous interaction networks, and also using the structure of the whole set of exogenous interaction networks. The experiments showed that some classifiers do better than others regarding both link classification and link ranking.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
