91滴滴

Efficient communication in many types of dynamic networks depends critically on how nodes are clustered into subnetworks. As such networks grow and evolve rapidly, there is a need for fast and robust clustering algorithms that account for communication cost structures induced by different node partitions. In this paper, we evaluate how classic community detection algorithms (CDAs), i.e. the Louvain and Ensemble Clustering for Graphs (ECG), adapted to incorporate a flexible family of cost functions, perform relative to standard heuristic and metaheuristic approaches.

Using simulations on l-nearest neighbour graphs of varying sizes, we find that especially the Louvain algorithm consistently delivers high quality solutions at a substantially lower computational cost.

In contrast, metaheuristic methods fail to scale effectively, and the ECG algorithm does not provide performance improvements in our setting, despite its reported stabilising effect in traditional community detection tasks.

Overall, our results indicate that classic CDAs are well suited for real time clustering in dynamic communication networks and constitute a strong basis for developing scalable communication optimisation strategies.

 Transport Networks, Communication Networks, Network Design, Graph Clustering

This research was funded in part through a generous contribution from as well as the grants from  and  .