Hierarchical community discovery for multi-stage IP bearer network upgradation

Abstract The huge and ever-increasing data traffic promotes the upgradation of IP bearer network to possess higher transmission capability. As a long-term task, network upgradation needs to be implemented in multiple stages. At each stage, one or more network partitions are planned to meet various requirements in different areas or periods to realize the smooth change from the current network to the targeted one. However, how to select proper network partitions is an open question. Existing approaches are usually infeasibly applied to large-scale networks or neglect the characteristics of IP bearer network. In this paper, we present a hierarchical community discovery method to find network partitions for nodes to be upgraded. We transform the issue of hierarchy identification on the whole network into node role classification, which is addressed by training a classifier utilizing network centrality, label, and attribute information. Then, a novel community detection algorithm through semi-local expansion is proposed, with the geographical location also taken into consideration. Experiments are conducted on three real-world datasets. The results show the effectiveness of our method in node role classification task that at least 90% Micro-F1 score can be got with only 30% labeled nodes on the given networks. The attained community is adjustable with the size proportional to the parameter α and has a high modularity score. Moreover, our communities strictly comply with the affiliation in IP bearer network and preserve the integrity of the tree/ring structures.