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This paper introduces SALT-GNN, a statistics-aware GNN architecture that fuses degree-aware statistical aggregation with attention to handle dense neighborhoods in anti-money laundering graphs, achieving improved F1 scores on dense recipient contexts with fewer parameters.
The paper proposes Label Influence Propagation (LIP), a model that analyzes and propagates label influences in graph neural networks for multi-label node classification, consistently outperforming state-of-the-art methods.
TAG-DLM unifies textual reasoning and graph message passing within a masked diffusion language model, enabling joint reasoning over text and graph topology for node classification and link prediction tasks.
Proposes DCQ-GNN, a spectral GNN that uses a compact bank of adaptive convex-concave quadratic filters to improve spectral selectivity without high-order polynomials, achieving competitive results on both homophilic and heterophilic graphs.
This paper proposes a K-Hop Gaussian (KHG) diffusion kernel as a preprocessing module for graph neural networks, balancing local and global information propagation to mitigate over-smoothing and information bottlenecks. Experiments show significant improvements over traditional message-passing GNNs and existing diffusion kernels, especially on noisy or structurally complex graphs.
KG-Guard is a lightweight graph-based framework for detecting hallucinations in LLM-based knowledge base question answering. It treats the LLM as a black box and uses a graph encoder with a MLP classifier to identify hallucinated answer nodes, outperforming baselines while having far fewer parameters.
Proposes DDGAD, a diffusion-based framework for graph anomaly detection that uses trajectory dynamics to distinguish normal from anomalous nodes, mitigating contamination propagation via a reliability-aware consensus mechanism and three complementary anomaly signals.
This paper introduces Transductive Sharpening (TS), a loss-level modification for semi-supervised node classification that minimizes prediction entropy on unlabeled nodes while counterbalancing on labeled nodes, achieving consistent performance improvements without architectural changes.
This paper introduces HAAM, a novel method for node classification in multiplex graphs that adapts to both homophilic and heterophilic interactions across dimensions. It uses dimension-specific compatibility matrices and a product of trainable low-pass and high-pass filters approximated via Chebyshev polynomials to capture smooth and abrupt signal changes.