  Many real-world applications operate on dynamic graphs that undergo rapid
changes in their topological structure over time. However, it is challenging to
design dynamic algorithms that are capable of supporting such graph changes
efficiently. To circumvent the challenge, we propose a batch-dynamic framework
for answering distance queries, which combines offline labelling and online
searching to leverage the advantages from both sides - accelerating query
processing through a partial distance labelling that is of limited size but
provides a good approximation to bound online searches. We devise batch-dynamic
algorithms to dynamize a distance labelling efficiently in order to reflect
batch updates on the underlying graph. In addition to providing theoretical
analysis for the correctness, labelling minimality, and computational
complexity, we have conducted experiments on 14 real-world networks to
empirically verify the efficiency and scalability of the proposed algorithms.
