Tie strength allows to classify social relationships and identify different types of them. For instance, social relationships can be classified as persistent and similar based respectively on the regularity with which they occur and the similarity among them. On the other hand, rare and somewhat similar relationships are random and cause noise in a social network, thus hiding the actual structure of the network and preventing an accurate analysis of it. In this article, we propose a method to handle social network data that exploits temporal features to improve the detection of communities by existing algorithms. By removing random relationships, we observe that social networks converge to a topology with more pure social relationships and better quality community structures.

中文翻译：

---

谁真正在我的社交圈中？

领带强度可以对社会关系进行分类并识别不同类型的社会关系。例如，可以根据社交关系发生的规律性和它们之间的相似性将社交关系分为持久性和相似性。另一方面，稀有且有些相似的关系是随机的，并在社交网络中引起噪音，从而隐藏了网络的实际结构并妨碍了对其的准确分析。在本文中，我们提出了一种处理社交网络数据的方法，该方法利用时间特征来改善现有算法对社区的检测。通过删除随机关系，我们观察到社交网络融合为具有更纯净社交关系和更好质量社区结构的拓扑。