Tag
This paper introduces a logic-based framework for extracting global logical rules from Simple Graph Convolution networks using minimal abductive explanations, aiming to provide compact and faithful explanations for node classification in graph neural networks.
This paper proposes MSB-GFM, a multi-semantic basis graph foundation model for cross-domain multi-label node classification, addressing semantic entanglement by representing nodes as adaptive compositions of semantic bases.
This paper introduces NodeJEPA, a joint-embedding predictive architecture for node-level graph self-supervised learning that predicts latent representations of masked structure-aware ego-subgraphs, avoiding reconstruction and hand-crafted augmentations. The method is evaluated on node classification benchmarks and shows competitive performance.
This paper systematically studies hypergraph node classification under label noise, proposing HyperTrust, a robust framework with HyperedgeBoost and HyperedgePrune modules, along with a unified benchmark for evaluating LLN and GLN methods on hypergraphs.
This paper introduces a nonlinear Laplacian operator for signed and directed graphs (NLSD) and a spectral GNN framework (NLSD-GNN) that achieves superior performance on node classification and link prediction by aligning message passing with edge direction.
Proposes ACE, a plug-and-play method that adaptively enhances coarsening-based GNN training on heterophilic graphs by reconstructing node features and applying anisotropic regularization, achieving consistent gains on heterophilic benchmarks with minimal overhead.
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.