Fast indexing algorithm for efficient k NN queries on complex networks

Abstract

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.

Indexing complex networks for fast attributed kNN queries

“…For instance, our methods generate an index for a social network with 1.6 million nodes within 5 seconds and finds exact kNN nodes within 1 second. Note that contributions (3), (4), (5), and (6) listed above include new materials compared to the conference version detailed in literature Kobayashi et al (2021). Although kNN queries are useful in real-life applications, their application to large attributed graphs is difficult because of the aforementioned challenges described in Sect.…”

“…The problem of finding kNN nodes on a graph has been studied in many fields in recent years, particularly in social network analysis and database communities (Zhong et al 2015;Li et al 2019;Abeywickrama and Cheema 2017;Lee et al 2012;Goldberg and Harrelson 2005;Kobayashi et al 2021). We categorize the related works as follows.…”