论文标题
探索和比较时间聚类方法
Exploring and comparing temporal clustering methods
论文作者
论文摘要
过去十年来，对时间网络的描述和动态社区的检测一直是研究的热门话题。但是，由于任务的复杂性，未发现对这些挑战的共识答案。静态群落不是定义明确的对象，并且添加时间维度会使描述更加困难。在本文中，我们提出了一种连贯的时间聚类方法：当地社区的最佳组合（BCLC）。我们的方法旨在在两个矛盾的目标之间找到良好的平衡：通过在每个时间步骤和时间平滑度中找到最佳分区来紧密遵循短时演变，这使历史连续性具有特权。
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.
