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Similarity search on social networks with incremental graph indexing based on probabilistic inference

Zhiwei Qi (Yunnan Key Laboratory of Intelligent Systems and Computing, School of Vocational and Continuing Education, Yunnan University, Kunming, China)
Tong Lu (Yunnan Key Laboratory of Intelligent Systems and Computing, School of Information Science and Engineering, Yunnan University, Kunming, China)
Kun Yue (Yunnan Key Laboratory of Intelligent Systems and Computing, School of Information Science and Engineering, Yunnan University, Kunming, China)
Liang Duan (Yunnan Key Laboratory of Intelligent Systems and Computing, School of Information Science and Engineering, Yunnan University, Kunming, China)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 28 June 2024

Issue publication date: 19 July 2024

69

Abstract

Purpose

This paper aims to propose an incremental graph indexing method based on probabilistic inferences in Bayesian network (BN) for approximate nearest neighbor search (ANNS) that adds unindexed queries into the graph index incrementally.

Design/methodology/approach

This paper first uses the attention mechanism based graph convolutional network to embed a social network into the low-dimensional vector space, which could improve the efficiency of graph index construction. To add the unindexed queries into the graph index incrementally, this study proposes to learn the rule-based BN from social interactions. Thus, the dependency relations of unindexed queries and their neighbors are represented, and the probabilistic inferences in BN are then performed.

Findings

Experimental results demonstrate that the proposed method improves the search precision by at least 5% and search efficiency by 10% compared to the state-of-the-art methods.

Originality/value

This paper proposes a novel method to construct the incremental graph index based on probabilistic inferences in BN, such that both indexed and unindexed queries in ANNS could be addressed efficiently.

Keywords

Acknowledgements

This paper was supported by the Key Program of Joint National Natural Science Foundation of China (U23A20298), Program of Yunnan Key Laboratory of Intelligent Systems and Computing (202205AG070003), Yunnan Fundamental Research Project (202301AT070193 and 202301AT070369) and Research Foundation of Educational Department of Yunnan Province (2023J0022).

Citation

Qi, Z., Lu, T., Yue, K. and Duan, L. (2024), "Similarity search on social networks with incremental graph indexing based on probabilistic inference", International Journal of Web Information Systems, Vol. 20 No. 4, pp. 395-412. https://doi.org/10.1108/IJWIS-12-2023-0255

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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