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Accelerated Graph Integration with Approximation of Combining Parameters
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Publication Year
2024-01-01
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer Science and Business Media Deutschland GmbH
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.14506 LNCS, pp.163-176
Keyword
Graph integrationGraph-based semi-supervised learningMaximum likelihood estimationNeumann series
Mesh Keyword
Complexes structureGraph integrationGraph-basedGraph-based modelsGraph-based semi-supervised learningIntegration methodIrregular structuresMaximum-likelihood estimationNeumann seriesSemi-supervised learning
All Science Classification Codes (ASJC)
Theoretical Computer ScienceComputer Science (all)
Abstract
Graph-based models offer the advantage of handling data that resides on irregular and complex structures. From various models for graph-structured data, graph-based semi-supervised learning (SSL) with label propagation has shown promising results in numerous applications. Meanwhile, with the rapid growth in the availability of data, there exist multiple relations for the same set of data points. Each relation contains complementary information to one another, and it would be beneficial to integrate all the available information. Such integration can be translated to finding an optimal combination of the graphs, and several studies have been conducted. Previous works, however, incur high computation time with a complex design of the learning process. This leads to a low capacity of applicability in multiple cases. To circumvent the difficulty, we propose an SSL-based fast graph integration method that employs approximation in the maximum likelihood estimation process of finding the combination. The proposed approximation utilizes the connection between the co-variance and its Neumann series, which allows us to avoid explicit matrix inversion. Empirically, the proposed method achieves competitive performance with significant improvements in computational time when compared to other integration methods.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37108
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85186264123&origin=inward
DOI
https://doi.org/10.1007/978-3-031-53966-4_13
Journal URL
https://www.springer.com/series/558
Type
Conference
Funding
Acknowledgements. This research was supported by Institute for Information communications Technology Promotion(IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00653, Voice Phishing Information Collection and Processing and Development of a Big Data Based Investigation Support System), BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991014091), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2003474) and the Ajou University research fund.This research was supported by Institute for Information communications Technology Promotion(IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00653, Voice Phishing Information Collection and Processing and Development of a Big Data Based Investigation Support System), BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Edu-cation (NRF5199991014091), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2003474) and the Ajou University research fund.
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