Canned patterns
(
i.e.
, small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling
pattern-at-a-time
construction mode. However, existing GUIS for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel, generic, and extensible framework called TATTOO that takes a data-driven approach to
automatically
select canned patterns for a GUI from large networks. Specifically, it first
decomposes
the underlying network into
truss-infested
and
truss-oblivious
regions. Then
candidate
canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified
plug
are then
selected
for the GUI from these candidates by maximizing
coverage
and
diversity
, and by minimizing the
cognitive load
of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing
plug-and-play
visual graph query interfaces for large networks.
Data-driven visual graph query interface construction and maintenance
