Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216224:14330/22:00127569

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural networks: the official journal of the International Neural Network Society

ISSN

0893-6080

e-ISSN

1879-2782

Svazek periodika

156

Číslo periodika v rámci svazku

December 2022

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

205-217

Kód UT WoS článku

000886066900007

EID výsledku v databázi Scopus

2-s2.0-85140088729