Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F22%3A00079087" target="_blank" >RIV/00209805:_____/22:00079087 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14330/22:00127569
Result on the web
<a href="https://reader.elsevier.com/reader/sd/pii/S0893608022003896?token=87052F21165A84F9A10A2D99E68C26C9B30322FAA8182BD2B19D0A8AD0A1CAEB35F609AC4BEA2238E580F23CB27042A7&originRegion=eu-west-1&originCreation=20221110130755" target="_blank" >https://reader.elsevier.com/reader/sd/pii/S0893608022003896?token=87052F21165A84F9A10A2D99E68C26C9B30322FAA8182BD2B19D0A8AD0A1CAEB35F609AC4BEA2238E580F23CB27042A7&originRegion=eu-west-1&originCreation=20221110130755</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neunet.2022.10.005" target="_blank" >10.1016/j.neunet.2022.10.005</a>
Alternative languages
Result language
angličtina
Original language name
Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural networks: the official journal of the International Neural Network Society
ISSN
0893-6080
e-ISSN
1879-2782
Volume of the periodical
156
Issue of the periodical within the volume
December 2022
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
205-217
UT code for WoS article
000886066900007
EID of the result in the Scopus database
2-s2.0-85140088729