  A structural graph summary is a small graph representation that preserves
structural information necessary for a given task. The summary is used instead
of the original graph to complete the task faster. We introduce multi-view
structural graph summaries and propose an algorithm for merging two summaries.
We conduct a theoretical analysis of our algorithm. We run experiments on three
datasets, contributing two new ones. The datasets are of different domains (web
graph, source code, and news) and sizes; the interpretation of multi-view
depends on the domain and are pay-level domains on the web, control vs.\@ data
flow of the code, and news broadcasters. We experiment with three graph summary
models: attribute collection, class collection, and their combination. We
observe that merging two structural summaries has an upper bound of quadratic
complexity; but under reasonable assumptions, it has linear-time worst-case
complexity. The running time of merging has a strong linear correlation with
the number of edges in the two summaries. Therefore, the experiments support
the assumption that the upper bound of quadratic complexity is not tight and
that linear complexity is possible. Furthermore, our experiments show that
always merging the two smallest summaries by the number of edges is the most
efficient strategy for merging multiple structural summaries.
