EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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A betweenness centrality for stochastic networks is defined via an absorbing Markov chain on sequences of reported central nodes, with importance given by pre-absorption occupancy and estimated by Monte Carlo.
The paper proposes and analyzes a distributed perception mechanism in Friedkin-Johnsen networks that enables convergence to true social power through local interactions in static and reflected-appraisal settings.
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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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Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach
A betweenness centrality for stochastic networks is defined via an absorbing Markov chain on sequences of reported central nodes, with importance given by pre-absorption occupancy and estimated by Monte Carlo.
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Dynamical models for distributed social power perception in Friedkin-Johnsen influence networks
The paper proposes and analyzes a distributed perception mechanism in Friedkin-Johnsen networks that enables convergence to true social power through local interactions in static and reflected-appraisal settings.