TAG-DLM integrates graph message passing into masked diffusion language models via topology attention masks on linearized neighborhoods, enabling prompt-based adaptation for node classification, link prediction, and transfer on text-attributed graphs.
International Conference on Learning Representations (ICLR) , year =
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GraphNetz supplies an automated statistical pipeline for GNN benchmarking that includes per-cell confidence intervals, paired tests with multiple-comparison correction, and critical-difference diagrams across tasks and datasets.
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TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
TAG-DLM integrates graph message passing into masked diffusion language models via topology attention masks on linearized neighborhoods, enabling prompt-based adaptation for node classification, link prediction, and transfer on text-attributed graphs.
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GraphNetz: Statistical Benchmarking of Graph Neural Networks with Paired Tests and Rank Aggregation
GraphNetz supplies an automated statistical pipeline for GNN benchmarking that includes per-cell confidence intervals, paired tests with multiple-comparison correction, and critical-difference diagrams across tasks and datasets.