APL-LLM: adaptive pseudo-labeling with large language models for few-shot node classification

Junyi Li; Chenweinan Jiang; Daixin Wang; Guo Ye; Libang Zhang; Huimei He; Binbin Hu; Zhiqiang Zhang; Fuzhen Zhuang

doi:10.1007/s11704-025-51174-6

APL-LLM: adaptive pseudo-labeling with large language models for few-shot node classification

Junyi Li
, Chenweinan Jiang
, Daixin Wang
, Guo Ye
, Libang Zhang
, Huimei He
, Binbin Hu
, Zhiqiang Zhang
, Fuzhen Zhuang^*

^*Corresponding author for this work

School of Artificial Intelligence(Institute of Artificial Intelligence)

Research output: Contribution to journal › Letter › peer-review

Original language	English
Article number	2012365
Journal	Frontiers of Computer Science
Volume	20
Issue number	12
DOIs	https://doi.org/10.1007/s11704-025-51174-6
State	Published - Dec 2026

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10.1007/s11704-025-51174-6

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@article{c12300bf25544bd78467d5c34c6e0305,

title = "APL-LLM: adaptive pseudo-labeling with large language models for few-shot node classification",

author = "Junyi Li and Chenweinan Jiang and Daixin Wang and Guo Ye and Libang Zhang and Huimei He and Binbin Hu and Zhiqiang Zhang and Fuzhen Zhuang",

year = "2026",

month = dec,

doi = "10.1007/s11704-025-51174-6",

language = "英语",

volume = "20",

journal = "Frontiers of Computer Science",

issn = "2095-2228",

publisher = "Higher Education Press Limited Company",

number = "12",

}

TY - JOUR

T1 - APL-LLM

T2 - adaptive pseudo-labeling with large language models for few-shot node classification

AU - Li, Junyi

AU - Jiang, Chenweinan

AU - Wang, Daixin

AU - Ye, Guo

AU - Zhang, Libang

AU - He, Huimei

AU - Hu, Binbin

AU - Zhang, Zhiqiang

AU - Zhuang, Fuzhen

PY - 2026/12

Y1 - 2026/12

UR - https://www.scopus.com/pages/publications/105030614100

U2 - 10.1007/s11704-025-51174-6

DO - 10.1007/s11704-025-51174-6

M3 - 快报

AN - SCOPUS:105030614100

SN - 2095-2228

VL - 20

JO - Frontiers of Computer Science

JF - Frontiers of Computer Science

IS - 12

M1 - 2012365

ER -

APL-LLM: adaptive pseudo-labeling with large language models for few-shot node classification

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