AV
A. Vlas
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
1 records found
1
Evaluating Graph Neural Additive Networks for Multi-Label Node Classification
How does Graph Neural Additive Network (GNAN) perform on different multi-label node classification datasets, and what do the resulting explanations reveal about the data?
Graph Neural Additive Networks (GNANs) extend generalised additive models to graph-structured data, providing interpretability by design rather than through post-hoc explanation. GNANs have been studied on multi-class node classification, but not in the multi-label setting, where a single node may belong to several categories at once. This paper presents the first adaptation and evaluation of GNAN for multi-label node classification. We replace the softmax output with a per-label sigmoid and give the distance function a per-label output, and we benchmark the adapted model against standard baselines on two real-world graphs that span a high- and a low-homophily regime, reporting Average Precision (AP) as the primary metric. We then analyse the learned shape functions and distance function to ask whether the built-in explanations are meaningful. GNAN is competitive with strong message-passing graph neural networks on the high-homophily graph, coming within about four AP points of the best baseline, but it drops to the lower end of the baselines on the low-homophily graph. Its learned distance function adapts to label homophily: a steep, reproducible local decay when neighbours are informative, and a flat, unstable profile when they are not. These results characterise when GNAN’s additive structure is an advantage and when it is a limitation, and they demonstrate the practical value of interpretability by design in the multi-label graph setting.
...
Graph Neural Additive Networks (GNANs) extend generalised additive models to graph-structured data, providing interpretability by design rather than through post-hoc explanation. GNANs have been studied on multi-class node classification, but not in the multi-label setting, where a single node may belong to several categories at once. This paper presents the first adaptation and evaluation of GNAN for multi-label node classification. We replace the softmax output with a per-label sigmoid and give the distance function a per-label output, and we benchmark the adapted model against standard baselines on two real-world graphs that span a high- and a low-homophily regime, reporting Average Precision (AP) as the primary metric. We then analyse the learned shape functions and distance function to ask whether the built-in explanations are meaningful. GNAN is competitive with strong message-passing graph neural networks on the high-homophily graph, coming within about four AP points of the best baseline, but it drops to the lower end of the baselines on the low-homophily graph. Its learned distance function adapts to label homophily: a steep, reproducible local decay when neighbours are informative, and a flat, unstable profile when they are not. These results characterise when GNAN’s additive structure is an advantage and when it is a limitation, and they demonstrate the practical value of interpretability by design in the multi-label graph setting.