Subgraph Filtering for Fair Graph Neural Networks is proposed, a lightweight and architecture-agnostic framework that mitigates structural bias at its source and achieves consistent fairness improvements while maintaining competitive predictive performance, leading to a better fairness--accuracy trade-off than recent fairness-aware GNN baselines.
Abstract
Graph neural networks (GNNs) can exhibit unfair behavior even when sensitive attributes are excluded from node features, because graph topology and message passing propagate group-correlated signals under sensitive homophily. Existing fairness-aware GNN methods mainly constrain representations or prediction distributions at a global level, without explicitly controlling the local structural pathways through which biased information propagates during aggregation. We propose Subgraph Filtering for Fair Graph Neural Networks (SF-GNN), a lightweight and architecture-agnostic framework that mitigates structural bias at its source. SF-GNN identifies bias-prone edges by combining sensitive homophily with structural propagation amplifiers, including hub participation and triadic closure. It then incorporates stochastic edge filtering into each message-passing step to selectively downweight or remove these edges while preserving the remaining graph structure. Training further incorporates a statistical-parity regularizer with a warm-up schedule to stabilize optimization. Experiments on five benchmark datasets show that SF-GNN achieves consistent fairness improvements while maintaining competitive predictive performance, leading to a better fairness--accuracy trade-off than recent fairness-aware GNN baselines.
We propose SGS-GNN, a supervised graph sparsifier for Graph Neural Networks (GNNs) to improve predictive performance and reduce the cost of message passing by removing task-irrelevant edges. Existing unsupervised sparsifiers are not task-aware, while existing supervised sparsifiers suffer from significant memory overhead, poor sparsity control, and a lack of homophily/heterophily awareness. SGS-GNN addresses these limitations by adopting a feature- and structure-aware edge-probability encoder, a sparse subgraph sampler that strictly adheres to a global sparsity constraint, and a homophily-aware regularizer to improve prediction accuracy across homophilic and heterophilic graphs. A key scalability-enhancing feature of SGS-GNN is that it ensures encoder updates are computed by backpropagating through the sampled subgraph, and avoids retaining edge-level computation graphs for all edges via gradient checkpointing. A key efficiency-enhancing feature of SGS-GNN is that the edge-probability encoder is updated only when it outperforms a degree-based edge sampler, ensuring performance no worse than a strong unsupervised baseline. Experiments on 33 homophilic and heterophilic graphs show that SGS-GNN improves F1-scores by 4% relative to full training and up to 30% on heterophilic graphs. Furthermore, SGS-GNN outperforms state-of-the-art methods by 4–7% at similar sparsity levels while reducing peak memory usage by up to 3.9×.
Siddhartha Shankar Das, Naheed Anjum Arafat, Muftiqur Rahman et al.· Proceedings of the 32nd ACM...· 0 citations
This work provides theoretical guarantees for the selective blocking rule by deriving the signed-versus-blocked decision boundary, separating signed-matrix effects from nonnegative block-operator spectral arguments, and showing when adaptive blocking preserves class-dependent components.
Jiho Choi, Yumin Lee, Chanran Kim et al.· ACM Transactions on Intellig...· 0 citations
CoRe-GNN is proposed, which performs both propagations in parallel at each layer: a coarsened inter-cluster term capturing long-range structure, and a local intra-cluster term preserving per-node discriminability.
Antonin Joly, Nicolas Keriven, Aline Roumy· 0 citations
Noise Corruption is introduced, a Noise Corruption-based explanation framework, which perturbs each message through matched-norm random-direction corruption while preserving the expected squared message norm, and NICE, a Noise Corruption-based explanation framework, which learns a Stochastic Restoration Boundary under NC-induced uncertainty, balancing target-prediction restoration against compactness.
Ziluowen Luo, Jun Yin, Ruochen Liu et al.· 0 citations
Graph neural networks learn from relational structure but can be sensitive to edge-level noise. We study a hybrid graph classifier that combines a message-passing branch (Graph Isomorphism Network, GIN) with a topological branch based on extended persistence diagrams and PersLay embeddings. Training optionally uses a paper-specific hinge penalty motivated by stable persistence-diagram representations and Lipschitz regularity, which we abbreviate as “HK-inspired.” On six TUDataset benchmarks, we compare five ablations (Full, GIN+PersLay, GIN + HK, GIN only, PersLay only) and measure robustness as accuracy drop under 10% random edge removal at test time, with persistence diagrams fixed from the original graphs; we also report targeted perturbations on MUTAG and PROTEINS and runtime on all six datasets. The regularized configurations reduce relative accuracy degradation where GIN-only is sensitive, particularly on smaller molecular and protein graphs, but do not consistently maximize clean or perturbed accuracy. On large social-network benchmarks, robustness differences are small and clean accuracy is the main differentiator. Adding PersLay to GIN can improve accuracy on several datasets, while PersLay alone performs worst. Training the full model increases per-epoch cost relative to GIN-only, with most overhead during training rather than inference. Overall, the results support a conditional empirical conclusion: combining structure, topology, and stability-oriented training can improve robustness under specified structural perturbations, with dataset-dependent accuracy–stability trade-offs rather than universal guarantees.
Jelena Losic, Charles Fanning· PLOS Complex Systems· 0 citations
DIAL, a message-passing layer that gives nodes access to graph structure through the diagonal of graph-derived operators, is introduced, which uses randomized probing to provide nodes with learnable, permutation equivariant access to diagonal entries.
Saku Peltonen, H. Bilgi, Kubilay Atasu· 0 citations
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