While the temporal networks have a wide range of applications such as opportunistic communication, there are not many clustering algorithms specifically proposed for them. Based on betweenness centrality for periodic graphs, we give a clustering pseudo-polynomial time algorithm for temporal networks, in which the transit value is always positive and the least common multiple of all transit values is bounded. Our experimental results show that the centrality of networks with 125 nodes and 455 edges can be efficiently computed in 3.2 s. Not only the clustering results using the infinite betweenness centrality for this kind of networks are better, but also the nodes with biggest influences are more precisely detected when the betweenness centrality is computed over the periodic graph. The algorithm provides a better result for temporal social networks with an acceptable running time.

中文翻译：

---

使用中介中心性对一维周期性网络进行聚类。

虽然时间网络具有广泛的应用，例如机会通信，但没有很多专门针对它们提出的聚类算法。基于周期图的介数中心性，我们给出了一种时间网络的聚类伪多项式时间算法，其中传输值始终为正，所有传输值的最小公倍数有界。我们的实验结果表明，具有 125 个节点和 455 个边的网络的中心性可以在 3.2 秒内有效计算。不仅使用无限介数中心性对此类网络的聚类结果更好，而且在周期图上计算介数中心性时，可以更准确地检测到影响最大的节点。