探索和比较时态聚类方法
Exploring and comparing temporal clustering methods
摘要：
时态网络的描述和动态社区的发现是近十年来的研究热点。然而，由于任务的复杂性，尚未找到应对这些挑战的共识答案。静态社区不是定义良好的对象，添加时间维度会使描述变得更加困难。在这篇文章中，我们提出了一个连贯的时间聚类方法：当地社区的最佳组合（BCLC）。我们的方法的目的是在两个相互冲突的目标之间找到一个很好的平衡：密切关注短时间的演变，找到最佳的分区在每个时间步和时间平滑，特权的历史连续性。
英文摘要：
Description of temporal networks and detection of dynamic communities have been hot topics of research for the last decade. However, no consensual answers to these challenges have been found due to the complexity of the task. Static communities are not well defined objects, and adding a temporal dimension renders the description even more difficult. In this article, we propose a coherent temporal clustering method: the Best Combination of Local Communities (BCLC). Our method aims at finding a good balance between two conflicting objectives : closely following the short time evolution by finding optimal partitions at each time step and temporal smoothness, which privileges historical continuity.
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。
