Abstract
In the real world, various applications can be modeled as large-scale label graphs, which labels can be represented the feature of nodes. Subgraph query is a problem of finding isomorphism subgraphs that satisfy the query conditions, which has attracted much attention in recent years. Therefore, a subgraph query method based on adjacent node features on large-scale label graphs with fewer label categories was proposed in this paper. The method consists of two phases, which are offline index creation and online query. Firstly, we introduce a data structure to describe the information of nodes by scalar features. Secondly, we propose a three-level star structure features index include adjacent nodes’ label category, the degree of star-centered and the number of each label, which is named adjacent node scalar features index (ANSF index). After that, according to the structure of query graph is star structure or not, we proposed two different processing strategies on the phase of online query. Experimental results show the efficiency and the effectiveness of the proposed method in index creation time, space usage and subgraph query.
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Acknowledgements
This work was supported by National Natural Science Foundation of China under Grant (Nos. 61472169, 61502215, 61802160, 51704138); The Key Research and Development Program of Liaoning Province (No. 2017231011); Shenyang City Young Science and Technology Innovation Talents Support Program (No. RC180244); Liaoning Public Opinion and Network Security Big Data System Engineering Laboratory (No. 04-2016-0089013); Scientific Research Fund of Liaoning Province Education Department (No. LYB201617).
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Shan, X., Ma, J., Gao, J., Xu, Z., Song, B. (2019). A Subgraph Query Method Based on Adjacent Node Features on Large-Scale Label Graphs. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_24
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DOI: https://doi.org/10.1007/978-3-030-30952-7_24
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