List sampling for large graphs

Yousuf, Muhammad Irfan; Kim, Suhyun

doi:10.3233/ida-163319

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…List Sampling (LS): The work [12] claims that the previous methods do not explore the neighborhood of sampled nodes fairly and hence yield sub-optimal samples. It then introduces a new approach in which we keep a list of candidate nodes that is populated with all the neighbors of nodes that have been sampled so far.…”

Section: Graph Samplingmentioning

confidence: 99%

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Empirical Characterization of Graph Sampling Algorithms

Yousuf

Anwer

Anwar

2022

Preprint

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Graph sampling allows mining a small representative subgraph from a big graph. Sampling algorithms deploy different strategies to replicate the properties of a given graph in the sampled graph. In this study, we provide a comprehensive empirical characterization of five graph sampling algorithms on six properties of a graph including degree, clustering coefficient, path length, global clustering coefficient, assorta-tivity, and modularity. We extract samples from fifteen graphs grouped into five categories including collaboration, social, citation, technological , and synthetic graphs. We provide both qualitative and quantitative results. We find that there is no single method that extracts true samples from a given graph with respect to the properties tested in this work. Our results show that the sampling algorithm that aggressively explores the neighborhood of a sampled node performs better than the others.

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Section: Graph Samplingmentioning

confidence: 99%

“…The paper proposes three algorithms based on this idea that differ in how to select nodes from the candidate list. We implement LS2 algorithm in this work as it performs better than the other two variations [12]. Hybrid Jump (HJ): HJ [13] introduces a hybrid jump strategy into Metropolis-Hasting Random Walk during the sampling process.…”

Section: Graph Samplingmentioning

confidence: 99%

Empirical Characterization of Graph Sampling Algorithms

Yousuf

Anwer

Anwar

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…List Sampling (LS): The work (Yousuf and Kim 2018) claims that the previous methods do not explore the neighborhood of sampled nodes fairly and hence yield sub-optimal samples. It then introduces a new approach in which we keep a list of candidate nodes that is populated with all the neighbors of nodes that have been sampled so far.…”

Section: Graph Samplingmentioning

confidence: 99%

Section: Graph Samplingmentioning

confidence: 99%

Empirical Characterization of Graph Sampling Algorithms

Yousuf¹,

Anwer²,

Anwar³

2021

Preprint

Self Cite

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Graph sampling allows mining a small representative subgraph from a big graph. Sampling algorithms deploy different strategies to replicate the properties of a given graph in the sampled graph. In this study, we provide a comprehensive empirical characterization of five graph sampling algorithms on six properties of a graph including degree, clustering coefficient, path length, global clustering coefficient, assortativity, and modularity. We extract samples from fifteen graphs grouped into five categories including collaboration, social, citation, technological, and synthetic graphs. We provide both qualitative and quantitative results. We find that there is no single method that extracts true samples from a given graph with respect to the properties tested in this work. Our results show that the sampling algorithm that aggressively explores the neighborhood of a sampled node performs better than the others.

show abstract

Empirical characterization of graph sampling algorithms

Yousuf

Anwer

Anwar

2023

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

Graph sampling allows mining a small representative subgraph from a big graph. Sampling algorithms deploy different strategies to replicate the properties of a given graph in the sampled graph. In this study, we provide a comprehensive empirical characterization of five graph sampling algorithms on six properties of a graph including degree, clustering coefficient, path length, global clustering coefficient, assortativity, and modularity. We extract samples from fifteen graphs grouped into five categories including collaboration, social, citation, technological, and synthetic graphs. We provide both qualitative and quantitative results. We find that there is no single method that extracts true samples from a given graph with respect to the properties tested in this work. Our results show that the sampling algorithm that aggressively explores the neighborhood of a sampled node performs better than the others.

show abstract

List sampling for large graphs

Cited by 7 publications

References 21 publications

Empirical Characterization of Graph Sampling Algorithms

Empirical Characterization of Graph Sampling Algorithms

Empirical Characterization of Graph Sampling Algorithms

Empirical characterization of graph sampling algorithms

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