面向即插即用的可视化图形查询接口：数据驱动的大型网络封装模式选择
Towards Plug-and-Play Visual Graph Query Interfaces: Data-driven Canned Pattern Selection for Large Networks
摘要：
可视化图形查询接口（也称为GUI）中的封装模式（即小的子图模式）通过启用每次模式构造模式来促进高效的查询公式化。然而，用于查询大型网络的现有GUI要么不暴露任何固定模式，要么如果暴露，则通常基于领域知识手动选择。不幸的是，人工生成的罐头模式不仅是劳动密集型的，但也可能缺乏多样性，以支持有效的视觉制定的广泛的子图查询。在本文中，我们提出了一种新的通用和可扩展的框架TATTOO，它采用数据驱动的方法来自动选择罐装模式的GUI从大型网络。具体来说，它首先将底层网络分解为桁架出没和桁架遗忘区域。然后从这些区域中生成捕获不同真实世界查询拓扑的候选罐装模式。然后，通过最大化覆盖率和多样性，并通过最小化模式集的认知负荷，从这些候选者中为GUI选择基于用户指定的插头的罐装模式。对真实世界数据集的实验研究证明了TATTOO的好处。重要的是，这项工作朝着实现大型网络的即插即用可视化图形查询接口迈出了具体的一步。
英文摘要：
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 selecting 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
