arXiv Machine Learning
Jul 13
Pattern-Aware Graph Neural Networks for Handling Missing Data
arXiv:2607. 08915v1 Announce Type: new Abstract: Missing data is ubiquitous in real-world datasets.
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features
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arXiv Machine Learning
Sep 29
Robust Graph Clustering Network for Multiple Missing Data
The paper introduces the Robust Graph Clustering Network for Multiple Missing Data (RGCN), a method designed to cluster graphs with simultaneous missing node attributes and structural links. RGCN employs a view‑decoupled dual‑branch imputation to reduce cross‑view interference, a multi‑hyperspherical mixture prior to improve cluster compactness and separability on a directional latent manifold, and a boundary‑aware contrastive enhancement objective to counteract cluster blurring caused by imputation bias. Experiments on real‑world datasets show that RGCN consistently outperforms state‑of‑the‑art baselines across various missing data patterns.
arXiv AI
Oct 15
Learning Higher-Order Interactions from Incomplete Spatiotemporal Data with Mixtures of Hypergraph Laplacians
The paper introduces MoHL, a mixture of hypergraph Laplacians that learns group-wise relational structure directly from incomplete spatiotemporal traffic sensor data. MoHL discovers overlapping sensor groups, constructs a Laplacian component for each interaction order, and learns their mixture through holdouts that mimic missingness patterns, sharing weights across group-induced couplings to pool evidence. Experiments on three traffic networks show that MoHL achieves the lowest error under day-long gaps, with far fewer learned parameters than deep imputers, demonstrating the benefit of higher-order groups and shared weights when relational evidence is scarce.
arXiv Machine Learning
4d ago
Diagnosing GNN Failure Modes with Layer-Wise Probe Entropy
The paper introduces a post-hoc diagnostic for graph neural networks that freezes a trained model, fits a linear probe to each layer, and tracks probe accuracy with normalized predictive entropy. By examining layer-wise probe entropy, the authors can distinguish beneficial, neutral, and harmful smoothing effects—something existing geometric measures cannot do. The study also shows that entropy degradation patterns separate two main GNN failure modes: structural mismatch under heterophily, which appears early even in shallow models, and excessive-depth oversmoothing that emerges only as propagation depth increases.
arXiv Machine Learning
Jun 8
Spatiotemporal Imputation with Graph-Informed Flow Matching
arXiv:2606. 06682v1 Announce Type: new Abstract: Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management.
arXiv AI
Sep 30
ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation
arXiv:2609.37632v1 Announce Type: cross Abstract: Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent perform...
arXiv AI
Aug 18
NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption
arXiv:2608. 16038v1 Announce Type: cross Abstract: Post-hoc Graph Neural Network (GNN) explainers commonly follow a Perturb-Query paradigm, inferring the importance of graph elements based on queried predictions to perturbed inputs.
arXiv Machine Learning
Aug 19
Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks
The paper introduces an asynchronous message‑passing framework for Graph Neural Networks to mitigate oversquashing, a problem where distant nodes cannot effectively communicate due to structural bottlenecks. Unlike conventional synchronous updates, the method updates a centrality‑guided batch of nodes at each layer, allowing information to propagate sequentially and reducing the need for increased channel capacity. Experiments on six standard and two long‑range graph classification benchmarks show notable performance gains, including 5 % improvement on REDDIT‑BINARY and 4 % on Peptides‑struct.
arXiv Machine Learning
Jun 9
PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
arXiv:2606. 09301v1 Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images.
arXiv Machine Learning
Sep 10
Not Just Oversmoothing: Detecting the Echo Chamber Effect in Graph Neural Networks
The paper introduces the Echo Chamber Effect, a failure mode in Graph Neural Networks where intra-community representations collapse while inter-community separation remains, differing from traditional oversmoothing. It proposes the Echo Chamber Index (ECI) to detect this effect by stratifying pairwise distances by community membership. Building on this analysis, the authors present Community-Aware Split Propagation (CASP), a lightweight plugin that decouples intra- and inter-community aggregation and learns their balance from label structure, improving performance across various GNN backbones in both homophilic and heterophilic settings.
arXiv Machine Learning
1d ago
Understanding and Mitigating Under-Confidence in GNNs from the Final Layer
arXiv:2505.11335v5 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness on graph-based tasks. However, their predictive confidence is often miscali...
arXiv Machine Learning
Oct 5
TSGuard: A Real-Time Framework for Detecting and Imputing Missing Data in Streaming Time Series
TSGuard is a real‑time framework that monitors, validates, and imputes missing values in streaming time series. It uses a lightweight graph‑aware temporal imputation model combined with constraint‑aware validation, fallback estimation, and operator‑facing explanations. The system integrates detection, imputation, and domain‑aware decision making—retaining or replacing values based on physical and spatial constraints—within a broader data‑quality loop, and offers interactive tools for operators to inspect and compare imputers in real time.