Cited By
View all- Chen TQiu DWu YKhan AKe XGao Y(2024)View-based Explanations for Graph Neural NetworksProceedings of the ACM on Management of Data10.1145/36392952:1(1-27)Online publication date: 26-Mar-2024
Automatic View Selection in Graph Databases
Authors: 
Chao Zhang, 
Jiaheng Lu, 
Qingsong Guo, Xinyong Zhang, Xiaochun Han, Minqi ZhouAuthors Info & Claims
Pages 197 - 202
Abstract
Recently, several works have studied the problem of view selection in graph databases. However, existing methods cannot fully exploit the graph properties of views, e.g., supergraph views and common subgraph views, which leads to a low view utility and duplicate view content. To address the problem, we propose an extended graph view that persists all the edge-induced subgraphs to answer the subgraph and supergraph queries simultaneously. Furthermore, we present the graph gene algorithm (GGA), which relies on a set of view transformations to reduce the view space and optimize the view benefit. Extensive experiments on real-life and synthetic datasets demonstrated GGA outperformed other selection methods in both effectiveness and efficiency.
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July 2021
275 pages
ISBN:9781450384131
DOI:10.1145/3468791
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Published: 11 August 2021
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SSDBM 2021
SSDBM 2021: 33rd International Conference on Scientific and Statistical Database Management
July 6 - 7, 2021
FL, Tampa, USA
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Overall Acceptance Rate 56 of 146 submissions, 38%
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View all- Chen TQiu DWu YKhan AKe XGao Y(2024)View-based Explanations for Graph Neural NetworksProceedings of the ACM on Management of Data10.1145/36392952:1(1-27)Online publication date: 26-Mar-2024
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