Vulnerable sections identification of distribution networks based on hybrid graph neural networks

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Publication Details

Journal
International Journal of Information and Communication Technology
Published
2026-01-01
DOI
https://doi.org/10.1504/ijict.2026.10077274
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

Vulnerable sections identification of distribution networks based on hybrid graph neural networks

Jun‐Wei Zhu, Jianbin Xu, He Zhang, Feng He et al.
International Journal of Information and Communication Technology
Advanced Graph Neural Networks
article

Vulnerable sections identification of distribution networks based on hybrid graph neural networks

Jun‐Wei Zhu, Jianbin Xu, He Zhang, Feng He, Ning Chen, Wei Lu
article en

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

International Journal of Information and Communication TechnologyVol. 27(32)
Openalex Percentile: Top 8%
Advanced Graph Neural Networks
DOIOpen Access PDFOpenAlex
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Vulnerable sections identification of distribution networks based on hybrid graph neural networks — Jun‐Wei Zhu, Jianbin Xu, et al. · International Journal of Information and Communication Technology (2026) | TGRS Research Map | TGRS