There is a growing need to explore attributed graphs such as social networks, expert networks, and biological networks. A well-known mechanism for non-technical users to explore such graphs is keyword search, which receives a set of query keywords and returns a connected subgraph that contains the keywords. However, existing approaches, such as methods based on shortest paths between nodes containing the query keywords, may generate weakly-connected answers as they ignore the structure of the whole graph. To address this issue, we formulate and solve the robust keyword search problem for attributed graphs to find strongly-connected answers. We prove that the problem is NP-hard and we propose a solution based on a random walk with restart (RWR). To improve the efficiency and scalability of RWR, we use Monte Carlo approximation and we also propose a distributed version, which we implement in Apache Spark. Finally, we provide experimental evidence of the efficiency and effectiveness of our approach on real-world graphs.

中文翻译：

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大型属性图中的强大关键字搜索

越来越需要探索诸如社交网络，专家网络和生物网络之类的归因图。非技术用户探索此类图的一种众所周知的机制是关键字搜索，它接收一组查询关键字并返回包含关键字的连接子图。但是，现有的方法（例如，基于包含查询关键字的节点之间的最短路径的方法）可能会生成弱连接的答案，因为它们会忽略整个图的结构。为了解决这个问题，我们制定并解决了强大的关键字搜索属性图查找紧密联系的答案的问题。我们证明了该问题是NP难题，并且我们提出了一种基于带重启随机游走（RWR）的解决方案。为了提高RWR的效率和可伸缩性，我们使用了蒙特卡洛近似，并且还提出了一个分布式版本，该版本在Apache Spark中实现。最后，我们提供了在现实世界图上该方法的效率和有效性的实验证据。