Graph Learning with Multiplicative Aggregation for Solar Filament Fragment Association Modeling in Full-disk H α Images
- 1Xihua University
- 2Chinese Academy of Sciences
- 3Southwest Jiaotong University
Abstract
Solar filaments are prominent features in H α observations. During their evolution, they often exhibit complex morphological and topological changes, which, due to line-of-sight projection effects, appear as fragmentation and merging. These phenomena make it difficult to reliably track solar filaments using automated methods, as the same structure may appear different over time. To address this issue, we formulate filament fragment association as a graph-based learning problem, where individual fragments are represented as nodes and candidate pairwise relationships are modeled as edges to infer whether fragments belong to the same filament. Furthermore, we propose a novel graph neural network (GNN) that incorporates multiplicative aggregation layers to enhance feature aggregation in the graph, replacing conventional additive aggregation with a multiplicative mechanism to better capture information between fragments. Experimental results demonstrate that the proposed method achieves reliable performance in filament fragment association, validating the effectiveness of the graph-based formulation and the proposed multiplicative aggregation mechanism.
Versions
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BibTeX
@article{zhu2026graph,
title = {{Graph Learning with Multiplicative Aggregation for Solar Filament Fragment Association Modeling in Full-disk H α Images}},
author = {GaoFei Zhu and GangHua Lin and Xiao Yang and Suo Liu and Yu Liu and ZhiSheng Gao and Cheng Zeng},
journal = {The Astrophysical Journal Supplement Series},
year = {2026},
month = sep,
doi = {10.3847/1538-4365/aea152},
url = {https://iopscience.iop.org/article/10.3847/1538-4365/aea152},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Graph Learning with Multiplicative Aggregation for Solar Filament Fragment Association Modeling in Full-disk H α Images
AU - GaoFei Zhu
AU - GangHua Lin
AU - Xiao Yang
AU - Suo Liu
AU - Yu Liu
AU - ZhiSheng Gao
AU - Cheng Zeng
PY - 2026
DA - 2026/09/30
JO - The Astrophysical Journal Supplement Series
DO - 10.3847/1538-4365/aea152
UR - https://iopscience.iop.org/article/10.3847/1538-4365/aea152
AB - Solar filaments are prominent features in H α observations. During their evolution, they often exhibit complex morphological and topological changes, which, due to line-of-sight projection effects, appear as fragmentation and merging. These phenomena make it difficult to reliably track solar filaments using automated methods, as the same structure may appear different over time. To address this issue, we formulate filament fragment association as a graph-based learning problem, where individual fragments are represented as nodes and candidate pairwise relationships are modeled as edges to infer whether fragments belong to the same filament. Furthermore, we propose a novel graph neural network (GNN) that incorporates multiplicative aggregation layers to enhance feature aggregation in the graph, replacing conventional additive aggregation with a multiplicative mechanism to better capture information between fragments. Experimental results demonstrate that the proposed method achieves reliable performance in filament fragment association, validating the effectiveness of the graph-based formulation and the proposed multiplicative aggregation mechanism.
SN - 0067-0049
SN - 1538-4365
ER -