B-cos GNNs achieve inherent explainability in graph neural networks by using linear aggregation and B-cos transforms to produce exact per-node per-feature contribution decompositions via dynamic linearity.
Discovering invariant rationales for graph neural networks
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.LG 4years
2026 4verdicts
UNVERDICTED 4roles
dataset 1polarities
use dataset 1representative citing papers
DSBD distills a dual-aligned structural basis to adapt GNNs across graphs with structural distribution shifts, outperforming prior methods on benchmarks.
CHCL aligns a Cheeger-Hodge joint signature across graph augmentations to produce embeddings that remain stable under local structural changes.
RIA uses adversarial exploration of counterfactual graph environments via label-invariant augmentations to improve OoD generalization in graph classification tasks.
citing papers explorer
-
B-cos GNNs: Faithful Explanations through Dynamic Linearity
B-cos GNNs achieve inherent explainability in graph neural networks by using linear aggregation and B-cos transforms to produce exact per-node per-feature contribution decompositions via dynamic linearity.
-
DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation
DSBD distills a dual-aligned structural basis to adapt GNNs across graphs with structural distribution shifts, outperforming prior methods on benchmarks.
-
Cheeger--Hodge Contrastive Learning for Structurally Robust Graph Representation Learning
CHCL aligns a Cheeger-Hodge joint signature across graph augmentations to produce embeddings that remain stable under local structural changes.
-
Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization
RIA uses adversarial exploration of counterfactual graph environments via label-invariant augmentations to improve OoD generalization in graph classification tasks.