TIMILSINA, Mohan, Vít NOVÁČEK, Mathieu DAQUIN and Haixuan YANG. Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding. NEURAL NETWORKS. ENGLAND: PERGAMON-ELSEVIER SCIENCE LTD, 2022, vol. 156, No 1, p. 205-217. ISSN 0893-6080. Available from: https://dx.doi.org/10.1016/j.neunet.2022.10.005.
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Basic information
Original name Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding
Name in Czech Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding
Authors TIMILSINA, Mohan, Vít NOVÁČEK (203 Czech Republic, guarantor, belonging to the institution), Mathieu DAQUIN and Haixuan YANG.
Edition NEURAL NETWORKS, ENGLAND, PERGAMON-ELSEVIER SCIENCE LTD, 2022, 0893-6080.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
WWW original online article
Impact factor Impact factor: 7.800
RIV identification code RIV/00216224:14330/22:00127569
Organization unit Faculty of Informatics
Doi http://dx.doi.org/10.1016/j.neunet.2022.10.005
UT WoS 000886066900007
Keywords in English diffusion; multi-layer embedding; neural network
Tags Artificial Intelligence, knowledge graphs, machine learning
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 28/3/2023 12:05.
Abstract
The scarcity of high-quality annotations in many application scenarios has recently led to an increasing interest in devising learning techniques that combine unlabeled data with labeled data in a network. In this work, we focus on the label propagation problem in multilayer networks. Our approach is inspired by the heat diffusion model, which shows usefulness in machine learning problems such as classification and dimensionality reduction. We propose a novel boundary-based heat diffusion algorithm that guarantees a closed-form solution with an efficient implementation. We experimentally validated our method on synthetic networks and five real-world multilayer network datasets representing scientific coauthorship, spreading drug adoption among physicians, two bibliographic networks, and a movie network. The results demonstrate the benefits of the proposed algorithm, where our boundary-based heat diffusion dominates the performance of the state-of-the-art methods.
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