A Hybrid Index for Distance Queries

Wang, Junhu; Anirban, Shikha; Amagasa, Toshiyuki; Shiokawa, Hiroaki; Gong, Zhiguo; Islam, Md. Saiful

doi:10.1007/978-3-030-62005-9_17

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Although indexing methods improve the efficiency of the kNN query, they cannot handle large complex networks for three reasons. First, indexing complex networks is expensive; this is because they are designed under the assumption that most networks are planar graphs Wang et al (2020). Their partitioning methods are efficient for planar graphs, such as road networks, but their assumptions are not suitable for complex networks with diverse structures Onizuka et al (2017).…”

Section: Existing Approaches and Challengesmentioning

confidence: 99%

Indexing complex networks for fast attributed kNN queries

Kobayashi

Matsugu

Shiokawa

2022

Soc. Netw. Anal. Min.

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The k nearest neighbor (kNN) query is an essential graph data-management tool used for finding relevant data entities suited to a user-specified query node. Graph indexing methods have the potential to achieve a quick kNN search response and thus are promising approaches. However, they struggle to handle large-scale attributed complex networks. This is because constructing indices and querying kNN nodes in large-scale networks are computationally expensive, and they are not designed to handle node attributes included in the networks. In this paper, we propose a novel graph indexing algorithm, namely CT index, for fast kNN queries on large complex networks. To overcome the aforementioned limitations, our algorithm generates two types of indices based on the topological properties of complex networks. In addition, we further propose BAG index along with CT index so that our algorithm enables to explore kNN nodes based on the attribute similarity. Our extensive experiments on real-world graphs show that our algorithm achieves up to 18,074 times faster indexing and 146 times faster kNN query than other state-of-the-art methods.

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Section: Existing Approaches and Challengesmentioning

confidence: 99%

Indexing complex networks for fast attributed kNN queries

Kobayashi

Matsugu

Shiokawa

2022

Soc. Netw. Anal. Min.

Self Cite

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show abstract

“…Although indexing methods improve kNN query efficiency, they cannot handle large complex networks due to two reasons. First, indexing complex networks is expensive because they are designed under the assumption that most networks are the planar graph [8]. Their partitioning methods are efficient for planar graphs such as road networks but their assumptions are not suitable for complex networks with diverse structures [9].…”

Section: A Existing Approaches and Challengesmentioning

confidence: 99%

Fast indexing algorithm for efficient k NN queries on complex networks

Kobayashi

Matsugu

Shiokawa

2021

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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k nearest neighbor (kNN) query is an essential graph data management tool to find relevant data entities suited to a user-specified query node. Graph indexing methods have the potential to achieve a quick kNN search response, the graph indexing methods are one of the promising approaches. However, they struggle to handle large-scale complex networks since constructing indexes and to querying kNN nodes in the largescale networks are computationally expensive. In this paper, we propose a novel graph indexing algorithm for a fast kNN query on large networks. To overcome the aforementioned limitations, our algorithm generates two types of indexes based on the topological properties of complex networks. Our extensive experiments on real-world graphs clarify that our algorithm achieves up to 18,074 times faster indexing and 146 times faster kNN query than the state-of-the-art methods.

show abstract