Journal ArticleDOI
Keyword Search over Distributed Graphs with Compressed Signature
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TLDR
A signature-based search algorithm is proposed that encodes the shortest-path distance from a vertex to any given keyword in the graph, and can find query answers by exploring fewer paths, so that the time and communication costs are low.Abstract:
Graph keyword search has drawn many research interests, since graph models can generally represent both structured and unstructured databases and keyword searches can extract valuable information for users without the knowledge of the underlying schema and query language. In practice, data graphs can be extremely large, e.g., a Web-scale graph containing billions of vertices. The state-of-the-art approaches employ centralized algorithms to process graph keyword searches, and thus they are infeasible for such large graphs, due to the limited computational power and storage space of a centralized server. To address this problem, we investigate keyword search for Web-scale graphs deployed in a distributed environment. We first give a naive search algorithm to answer the query efficiently. However, the naive search algorithm uses a flooding search strategy that incurs large time and network overhead. To remedy this shortcoming, we then propose a signature-based search algorithm. Specifically, we design a vertex signature that encodes the shortest-path distance from a vertex to any given keyword in the graph. As a result, we can find query answers by exploring fewer paths, so that the time and communication costs are low. Moreover, we reorganize the graph data in the cluster after its initial random partitioning so that the signature-based techniques are more effective. Finally, our experimental results demonstrate the feasibility of our proposed approach in performing keyword searches over Web-scale graph data.read more
Citations
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References
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Proceedings ArticleDOI
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Grzegorz Malewicz,Matthew H. Austern,Aart J. C. Bik,James C. Dehnert,Ilan Horn,Naty Leiser,Grzegorz Czajkowski +6 more
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Journal ArticleDOI
Multilevelk-way Partitioning Scheme for Irregular Graphs
George Karypis,Vipin Kumar +1 more
TL;DR: This paper presents and study a class of graph partitioning algorithms that reduces the size of the graph by collapsing vertices and edges, they find ak-way partitioning of the smaller graph, and then they uncoarsen and refine it to construct ak- way partitioning for the original graph.
Journal ArticleDOI
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Yucheng Low,Danny Bickson,Joseph E. Gonzalez,Carlos Guestrin,Aapo Kyrola,Joseph M. Hellerstein +5 more
TL;DR: GraphLab as discussed by the authors extends the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees to reduce network congestion and mitigate the effect of network latency in the shared-memory setting.