Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7% gains over language model baselines.
Kggen: Extracting knowledge graphs from plain text with language models
4 Pith papers cite this work. Polarity classification is still indexing.
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A dual-purpose benchmark supplies two text-derived knowledge graphs and one expert reference graph on the same biomedical corpus to jointly measure construction method quality and GNN robustness via semi-supervised node classification.
MicroWorld constructs a multimodal attributed property graph from scientific image-caption data and augments MLLM prompts via retrieval to raise Qwen3-VL-8B performance by 37.5% on MicroVQA and 6% on MicroBench.
Experiment-as-Code Labs encodes experiments as declarative configurations that AI agents generate, systems software analyzes and orchestrates, and device APIs execute on physical lab hardware.
citing papers explorer
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Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs
Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7% gains over language model baselines.
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A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks
A dual-purpose benchmark supplies two text-derived knowledge graphs and one expert reference graph on the same biomedical corpus to jointly measure construction method quality and GNN robustness via semi-supervised node classification.
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MicroWorld: Empowering Multimodal Large Language Models to Bridge the Microscopic Domain Gap with Multimodal Attribute Graph
MicroWorld constructs a multimodal attributed property graph from scientific image-caption data and augments MLLM prompts via retrieval to raise Qwen3-VL-8B performance by 37.5% on MicroVQA and 6% on MicroBench.
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Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
Experiment-as-Code Labs encodes experiments as declarative configurations that AI agents generate, systems software analyzes and orchestrates, and device APIs execute on physical lab hardware.