  We describe SynGraphy, a method for visually summarising the structure of
large network datasets that works by drawing smaller graphs generated to have
similar structural properties to the input graphs. Visualising complex networks
is crucial to understand and make sense of networked data and the relationships
it represents. Due to the large size of many networks, visualisation is
extremely difficult; the simple method of drawing large networks like those of
Facebook or Twitter leads to graphics that convey little or no information.
While modern graph layout algorithms can scale computationally to large
networks, their output tends to a common "hairball" look, which makes it
difficult to even distinguish different graphs from each other. Graph sampling
and graph coarsening techniques partially address these limitations but they
are only able to preserve a subset of the properties of the original graphs. In
this paper we take the problem of visualising large graphs from a novel
perspective: we leave the original graph's nodes and edges behind, and instead
summarise its properties such as the clustering coefficient and bipartivity by
generating a completely new graph whose structural properties match that of the
original graph. To verify the utility of this approach as compared to other
graph visualisation algorithms, we perform an experimental evaluation in which
we repeatedly asked experimental subjects (professionals in graph mining and
related areas) to determine which of two given graphs has a given structural
property and then assess which visualisation algorithm helped in identifying
the correct answer. Our summarisation approach SynGraphy compares favourably to
other techniques on a variety of networks.

Social and Information Networks;Machine Learning