Abstract
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.
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