Defines temporal conductance Φ for dynamic networks and proves the voter model consensus time is O(m/(d_min Φ)) with a tight lower bound.
On coalescence time in graphs: When is coalescing as fast as meeting?ACM Trans
3 Pith papers cite this work. Polarity classification is still indexing.
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Develops an FPRAS for consensus probabilities in voter models with agnostic nodes by combining martingale analysis with rumour-spreading bounds and MCMC estimation.
In finite-depth random linear optical circuits, entanglement grows at most diffusively and robust circuit complexity scales similarly, with depth bounds ensuring near-maximal subsystem entanglement and closeness to Haar unitaries.
citing papers explorer
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Temporal Conductance and Bounds on the Voter Model for Dynamic Networks
Defines temporal conductance Φ for dynamic networks and proves the voter model consensus time is O(m/(d_min Φ)) with a tight lower bound.
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Voter Model Meets Rumour Spreading: an FPRAS for Consensus Probabilities on Voter Models with Agnostic Nodes
Develops an FPRAS for consensus probabilities in voter models with agnostic nodes by combining martingale analysis with rumour-spreading bounds and MCMC estimation.
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Entanglement and circuit complexity in finite-depth random linear optical networks
In finite-depth random linear optical circuits, entanglement grows at most diffusively and robust circuit complexity scales similarly, with depth bounds ensuring near-maximal subsystem entanglement and closeness to Haar unitaries.