  Community detection is a fundamental problem in the analysis of complex
networks. It is the analogue of clustering in network data mining. Within
community detection methods, hierarchical algorithms are popular. However,
their iterative nature and the need to recompute the structural properties used
to split the network (i.e. edge betweenness in Girvan and Newman's algorithm),
make them unsuitable for large network data sets. In this paper, we study how
local structural network properties can be used as proxies to improve the
efficiency of hierarchical community detection while, at the same time,
achieving competitive results in terms of modularity. In particular, we study
the potential use of the structural properties commonly used to perform local
link prediction, a supervised learning problem where community structure is
relevant, as nodes are prone to establish new links with other nodes within
their communities. In addition, we check the performance impact of network
pruning heuristics as an ancillary tactic to make hierarchical community
detection more efficient
