# Link Prediction

Solar filaments in Hα images break into fragments as they evolve, and projection effects can separate pieces of one filament while bringing pieces of different filaments close together, so distance and orientation alone cannot decide which fragments belong together. The study treats each fragment as a graph node and each candidate pair as an edge, predicts at the edge level with a graph neural network, and replaces conventional additive aggregation with multiplicative aggregation so that inconsistent neighbors contribute less. On a dataset built from about 100 Kanzelhöhe Observatory Hα images and about 1,700 fragments, the method reaches AUC 0.867 and AP 0.792 at kNN 10, above GCN at 0.832/0.747 and GAT at 0.842/0.766.

Solar filaments in Hα images break into fragments as they evolve, and projection effects can separate pieces of one filament while bringing pieces of different filaments close together, so distance and orientation alone cannot decide which fragments belong together. The study treats each fragment as a graph node and each candidate pair as an edge, predicts at the edge level with a graph neural network, and replaces conventional additive aggregation with multiplicative aggregation so that inconsistent neighbors contribute less. On a dataset built from about 100 Kanzelhöhe Observatory Hα images and about 1,700 fragments, the method reaches AUC 0.867 and AP 0.792 at kNN 10, above GCN at 0.832/0.747 and GAT at 0.842/0.766.

Attention aggregation on heterogeneous graphs has long treated node types as static labels, and metapath aggregation can overlook the semantic contribution of intermediate nodes. TAHAN instead collects neighbors along metapath prefixes, merges them across metapaths and levels by node type, then fuses them with within-type attention followed by across-type attention. On DBLP and IMDB node classification its mean Macro-F1 and Micro-F1 exceed the listed baselines, with both IMDB gains significant after Holm correction; on ACM it falls below SeHGNN.

Attention aggregation on heterogeneous graphs has long treated node types as static labels, and metapath aggregation can overlook the semantic contribution of intermediate nodes. TAHAN instead collects neighbors along metapath prefixes, merges them across metapaths and levels by node type, then fuses them with within-type attention followed by across-type attention. On DBLP and IMDB node classification its mean Macro-F1 and Micro-F1 exceed the listed baselines, with both IMDB gains significant after Holm correction; on ACM it falls below SeHGNN.