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

J 2022

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

TIMILSINA, Mohan, Vít NOVÁČEK, Mathieu DAQUIN a Haixuan YANG

Základní údaje

Originální název

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

Název česky

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

Autoři

TIMILSINA, Mohan, Vít NOVÁČEK (203 Česká republika, garant, domácí), Mathieu DAQUIN a Haixuan YANG

Vydání

NEURAL NETWORKS, ENGLAND, PERGAMON-ELSEVIER SCIENCE LTD, 2022, 0893-6080

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

original online article

Impakt faktor

Impact factor: 7.800

Kód RIV

RIV/00216224:14330/22:00127569

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1016/j.neunet.2022.10.005

UT WoS

000886066900007

Klíčová slova anglicky

diffusion; multi-layer embedding; neural network

Štítky

Artificial Intelligence, knowledge graphs, machine learning

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 3. 2023 12:05, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Citovat

TIMILSINA, Mohan, Vít NOVÁČEK, Mathieu DAQUIN a Haixuan YANG. Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding. NEURAL NETWORKS. ENGLAND: PERGAMON-ELSEVIER SCIENCE LTD, 2022, roč. 156, č. 1, s. 205-217. ISSN 0893-6080. Dostupné z: https://dx.doi.org/10.1016/j.neunet.2022.10.005.

@article{2241658,
   author = {Timilsina, Mohan and Nováček, Vít and dAquin, Mathieu and Yang, Haixuan},
   article_location = {ENGLAND},
   article_number = {1},
   doi = {http://dx.doi.org/10.1016/j.neunet.2022.10.005},
   keywords = {diffusion; multi-layer embedding; neural network},
   language = {eng},
   issn = {0893-6080},
   journal = {NEURAL NETWORKS},
   title = {Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding},
   url = {https://www.sciencedirect.com/science/article/pii/S0893608022003896},
   volume = {156},
   year = {2022}
}

TY  - JOUR
ID  - 2241658
AU  - Timilsina, Mohan - Nováček, Vít - dAquin, Mathieu - Yang, Haixuan
PY  - 2022
TI  - Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding
JF  - NEURAL NETWORKS
VL  - 156
IS  - 1
SP  - 205-217
EP  - 205-217
PB  - PERGAMON-ELSEVIER SCIENCE LTD
SN  - 08936080
KW  - diffusion
KW  - multi-layer embedding
KW  - neural network
UR  - https://www.sciencedirect.com/science/article/pii/S0893608022003896
N2  - 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.
ER  -

TIMILSINA, Mohan, Vít NOVÁČEK, Mathieu DAQUIN a Haixuan YANG. Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding. \textit{NEURAL NETWORKS}. ENGLAND: PERGAMON-ELSEVIER SCIENCE LTD, 2022, roč.~156, č.~1, s.~205-217. ISSN~0893-6080. Dostupné z: https://dx.doi.org/10.1016/j.neunet.2022.10.005.

Podrobný výpis o publikaci