NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption

Subgraph Filtering for Fair Graph Neural Networks

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.

Local Geometry Improves Explanation Robustness for Graph Neural Networks

Out-of-Distribution Robust Explainer for Graph Neural Networks

Experiments on synthetic and real-world datasets show that ORExplainer consistently provides more robust explanations across diverse node-level OOD settings, outperforming existing baselines.

Uncertainty-Aware Adaptive Edge Blocking in Heterophilic Graph Neural Networks

Graph Neural Networks (GNNs) deliver strong performance through message passing (MP), yet they can become fragile when graphs contain many heterophilic links or when edge types are predicted incorrectly. Earlier analyses suggest that signed message propagation can reduce over-smoothing when the edge misclassification rate is low. However, many such analyses condition on accurately identified edge types and blur the distinction between neighbor propagation and self-retention. This work re-examines these assumptions and shows that under high uncertainty in edge labels, message passing can reduce node distinguishability even when signed edge weights are applied. The central insight is that uncertain edges should not always be propagated: it can be preferable to adaptively block a message when the estimated edge-type error outweighs the expected gain from signed propagation. Concretely, we analyze neighbor propagation without treating self-loops as edge-type observations, use a row-normalized operator for the neighbor process, and estimate two latent reliability factors during training: the local homophily and the edge classification error rate. To avoid leakage between reliability estimation and model selection, the edge error rate is estimated from a separate calibration split or from a train-only proxy, while validation labels are reserved for hyperparameter and checkpoint selection. We provide 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. Experiments on homophilic, heterophilic, and large-scale graphs show that the proposed strategy consistently improves signed GNN backbones and remains competitive with strong heterophily-oriented baselines. Compared with the UAI 2025 conference version [11], this journal version adds an extension analysis, tightened derivations, a leakage-free calibration protocol, and expanded empirical results.

The Hidden Fragility of GNNs: How Graph Structure Amplifies Numerical Errors

This work proposes Aggregation-Aware Representation Learning (AARL) to learn numerically stable and cancellation-resistant representations without sacrificing expressiveness, and formalizes this through the cancellation ratio, proving that it is fundamentally governed by graph topology.

Precision at Scale: An End-to-End Graph-based Framework for Mitigating Network Interference in TikTok A/B Tests

Large-scale A/B tests on social platforms suffer from inherent network interference, violating the Stable Unit Treatment Value Assumption (SUTVA) and distorting measured results. Industrial practices for mitigating network interference face a foundational trade-off. For scalability, they often rely on clustering static graphs, which serve as imperfect proxies for true interference pathways. Conversely, theoretically sound methods remain computationally intractable at production scale. This paper presents a production-ready framework deployed at TikTok, which integrates three core contributions to address these challenges: 1) Learned Interference Graph (LIG): Estimates interference probabilities using dynamic interaction patterns for more context-aware modeling. 2) Scalable Community Partitioning (SCP): A Spark-optimized ParLeiden implementation that performs billion-node graph clustering daily and generalizes effectively across diverse interaction types, achieving a purity score of 0.898 for group chat interactions. 3) Sensitivity-Enhanced Estimation (SEE): A multivariate system leveraging Controlled-experiment Using Pre-Experiment Data (CUPED) to mitigate variance inflation from cluster-based randomization. In live production tests, our framework reduces interference rates by 68.8%, correcting a biased treatment effect estimate from +1.44% to a statistically significant +2.08%. It also enables previously undetectable cross-ecosystem measurements, revealing a +0.2% lift in creator upload volumes driven by user-side treatments.