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Nicole Wein
Researcher at Massachusetts Institute of Technology
Publications - 37
Citations - 333
Nicole Wein is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Approximation algorithm & Exponential time hypothesis. The author has an hindex of 10, co-authored 32 publications receiving 207 citations.
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Towards tight approximation bounds for graph diameter and eccentricities
TL;DR: The lower bound for near-linear time algorithms is essentially tight by giving an algorithm that approximates Eccentricities within a 2+δ factor in Õ(m/δ) time for any 0<δ<1, which is the first lower bound in fine-grained complexity that addresses near- linear time computation.
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Towards Tight Approximation Bounds for Graph Diameter and Eccentricities
TL;DR: In this paper, it was shown that unless the Strong Exponential Time Hypothesis (SETH) fails, no algorithm can achieve an approximation factor better than $3/2$ in sparse graphs.
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Finding Cliques in Social Networks: A New Distribution-Free Model
TL;DR: In this article, the authors proposed a new distribution-free model of social networks, called "weakly $c$-closed" graphs, where for every pair of vertices $u,v$ with at least common neighbors, $u$ and $v$ are adjacent.
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New algorithms and hardness for incremental single-source shortest paths in directed graphs
TL;DR: A deterministic data structure for incremental SSSP in weighted directed graphs with total update time Õ(n 2 logW/є O(1)) which is near-optimal for very dense graphs; here W is the ratio of the largest weight in the graph to the smallest.
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Algorithms and Hardness for Diameter in Dynamic Graphs
TL;DR: In this paper, the authors provide a comprehensive study of the dynamic approximation of Diameter, Radius and Eccentricities, providing both conditional lower bounds, and new algorithms whose bounds are optimal under popular hypotheses in fine-grained complexity.