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This paper presents a comprehensive benchmark for evaluating adversarial attacks and defenses in Graph Neural Networks, highlighting the need for standardized and fair experimental protocols.
This paper introduces a unified benchmark to evaluate the robustness of Graph Neural Networks on noisy, text-derived knowledge graphs and the effectiveness of graph construction methods in the biomedical domain.
This paper introduces NATD-GSSL, a framework evaluating the robustness of Graph Self-Supervised Learning on noisy, text-driven biomedical graphs. It demonstrates that certain GNN architectures and pretext tasks maintain performance despite real-world noise, offering practical guidance for unsupervised learning in imperfect datasets.
This paper proposes Node-Edge Policy Factorization (NEPF) to address scalability issues in solving Vehicle Routing Problems on multigraphs. It combines pre-encoding edge aggregation with a hierarchical reinforcement learning method to achieve state-of-the-art solution quality with faster training and inference.
This paper introduces CopyCop, an algorithm for verifying ownership of Graph Neural Networks by detecting surrogate models even when they differ in architecture, weights, or output transformations.
MSR-MEL introduces an unsupervised framework using LLMs to synthesize and reason over multi-perspective evidence for multimodal entity linking, outperforming prior methods on standard benchmarks.
LLMSniffer is a detection framework that fine-tunes GraphCodeBERT with supervised contrastive learning to distinguish AI-generated code from human-written code, achieving 78% accuracy on GPTSniffer and 94.65% on Whodunit benchmarks. The approach addresses critical challenges in academic integrity and code quality assurance by combining code-structure-aware embeddings with contrastive learning and comment removal preprocessing.
This paper introduces HSG (Hyperbolic Scene Graph), a scene graph model that leverages hyperbolic geometry for representing hierarchical scene structures. It is hosted on Hugging Face and referenced via arXiv:2604.17454.