以稳定为重点的社区演变（扩展摘要）
Evolution of Communities with Focus on Stability (extended abstract)
摘要：
社区检测是分析手机用户社交图谱的重要工具。在每个社区内，客户都容易通过相互影响的杠杆作用吸引新客户，保留旧客户和/或接受新产品或服务。用户社区是更小的单位，更容易掌握，并允许例如角色分析的计算-基于参与者在其社区中的中心地位。在静态图中寻找社区的问题已经得到了广泛的研究。然而，从电信分析师的角度来看，要真正有用，检测到的社区必须随着通信的社交图随着时间的推移而变化-例如，为了对社区进行营销活动并跟踪这些活动的结果。此外，用户社区随时间的行为可以用于预测电信运营商感兴趣的未来活动，例如用户流失或手机采用。相似的群体演化可以为设计策略提供见解，例如群体流失的早期预警。稳定性是一个关键问题：如果分析员不能在随后的时间步骤中跟踪给定团体，则对该团体执行的分析将丢失。这是我们在本文中处理的特定用例：跟踪动态场景中社区的演变，重点关注稳定性。我们提出了两个修改广泛使用的静态社区发现算法。然后，我们描述的实验，以研究在现实世界的社交网络，每月调用图为数百万用户表示的分区的稳定性和质量。
英文摘要：
The detection of communities is an important tool used to analyze the social graph of mobile phone users. Within each community, customers are susceptible of attracting new ones, retaining old ones and/or accepting new products or services through the leverage of mutual influences. The communities of users are smaller units, easier to grasp, and allow for example the computation of role analysis -- based on the centrality of an actor within his community. The problem of finding communities in static graphs has been widely studied. However, from the point of view of a telecom analyst, to be really useful, the detected communities must evolve as the social graph of communications changes over time -- for example, in order to perform marketing actions on communities and track the results of those actions over time. Additionally the behaviors of communities of users over time can be used to predict future activity that interests the telecom operators, such as subscriber churn or handset adoption. Similary group evolution can provide insights for designing strategies, such as the early warning of group churn. Stability is a crucial issue: the analysis performed on a given community will be lost, if the analyst cannot keep track of this community in the following time steps. This is the particular use case that we tackle in this paper: tracking the evolution of communities in dynamic scenarios with focus on stability. We propose two modifications to a widely used static community detection algorithm. We then describe experiments to study the stability and quality of the resulting partitions on real-world social networks, represented by monthly call graphs for millions of subscribers.
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。
