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Canned patterns (i.e. small subgraph patterns) in visual graph queryinterfaces (a.k.a GUI) facilitate efficient query formulation by enablingpattern-at-a-time construction mode. However, existing GUIs for querying largenetworks either do not expose any canned patterns or if they do then they aretypically selected manually based on domain knowledge. Unfortunately, manualgeneration of canned patterns is not only labor intensive but may also lackdiversity for supporting efficient visual formulation of a wide range ofsubgraph queries. In this paper, we present a novel generic and extensibleframework called TATTOO that takes a data-driven approach to automaticallyselecting canned patterns for a GUI from large networks. Specifically, it firstdecomposes the underlying network into truss-infested and truss-obliviousregions. Then candidate canned patterns capturing different real-world querytopologies are generated from these regions. Canned patterns based on auser-specified plug are then selected for the GUI from these candidates bymaximizing coverage and diversity, and by minimizing the cognitive load of thepattern set. Experimental studies with real-world datasets demonstrate thebenefits of TATTOO. Importantly, this work takes a concrete step towardsrealizing plug-and-play visual graph query interfaces for large networks.

Towards Plug-and-Play Visual Graph Query Interfaces: Data-driven Canned Pattern Selection for Large Networks