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Isabelle Stanton
Researcher at University of California, Berkeley
Publications - 16
Citations - 928
Isabelle Stanton is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Streaming algorithm & Degree distribution. The author has an hindex of 9, co-authored 16 publications receiving 865 citations. Previous affiliations of Isabelle Stanton include Google & University of Virginia.
Papers
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Proceedings ArticleDOI
Streaming graph partitioning for large distributed graphs
Isabelle Stanton,Gabriel Kliot +1 more
TL;DR: This work proposes natural, simple heuristics for graph partitioning and compares their performance to hashing and METIS, a fast, offline heuristic, and shows on a large collection of graph datasets that they are a significant improvement.
Book ChapterDOI
Clustering social networks
TL;DR: This work introduces a new criterion that overcomes limitations by combining internal density with external sparsity in a natural way in order to find close-knit clusters in social networks.
Journal ArticleDOI
Constructing and sampling graphs with a prescribed joint degree distribution
Isabelle Stanton,Ali Pinar +1 more
TL;DR: An algorithm for constructing simple graphs from a given joint degree distribution, and a Monte Carlo Markov chain method for sampling them, and it is shown that the state space of simple graphs with a fixed degree distribution is connected via endpoint switches.
Journal ArticleDOI
Finding Strongly Knit Clusters in Social Networks
TL;DR: This paper introduces a new criterion that overcomes limitations by combining internal density with external sparsity in a natural way, and explores combinatorial properties of internally dense and externally sparse clusters.
Proceedings ArticleDOI
Streaming balanced graph partitioning algorithms for random graphs
TL;DR: This paper considers the problem of loading a graph onto a distributed cluster with the goal of optimizing later computation and gives lower bounds on this problem, showing that no algorithm can obtain an o(n) approximation with a random or adversarial stream ordering.