EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
Pro- ceedings of the IEEE 106(5), 808–828 (2018)
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SampEn_G generalizes sample entropy to graph signals via multi-hop graph embeddings based on the graph shift operator, reducing to the classical version on path graphs and showing sensitivity to nonlinear dynamics.
Sample entropy is extended to graph signals via topology-aware multi-hop embeddings to quantify nonlinear dynamics on networks.
Large vision-language models applied to multi-scale remote sensing imagery can generate recommendations on built environment design, constructability, land use, and risks for smart city decision-making.
citing papers explorer
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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Sample entropy for graph signals: An approach to nonlinear analysis of graph signals
SampEn_G generalizes sample entropy to graph signals via multi-hop graph embeddings based on the graph shift operator, reducing to the classical version on path graphs and showing sensitivity to nonlinear dynamics.
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Sample entropy for graph signals: An approach to nonlinear dynamic analysis of data on networks
Sample entropy is extended to graph signals via topology-aware multi-hop embeddings to quantify nonlinear dynamics on networks.
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Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models
Large vision-language models applied to multi-scale remote sensing imagery can generate recommendations on built environment design, constructability, land use, and risks for smart city decision-making.