node-classification

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.