A Novel Hybrid-Jump-Based Sampling Method for Complex Social Networks

Liu, Lianggui; Wang, Lingmin; Wu, Wei; Jia, Huiling

doi:10.1109/tcss.2019.2893889

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…Metropolis-Hastings random walk sampling strategies overcome this bias by making the random walker visit low-degree nodes (Hübler et al 2008;Stutzbach et al 2006;Li et al 2015). Liu et al (2019) incorporates a novel hybrid jump mechanism in Metropolis-Hastings random walk to avoid repetitive sampling within a small connected component. Forest Fire sampling, a hybrid of random walk-based methods and snowball sampling, expands by burning a fraction of the outgoing links for each sampled node (Leskovec and Faloutsos 2006).…”

Section: Sampling Social Media Networkmentioning

confidence: 99%

Tight Sampling in Unbounded Networks

Jaglan,

Pindiprolu,

Sharma

et al. 2024

ICWSM

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The default approach to deal with the enormous size and limited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnetworks will crucially depend on the sampling scheme. Motivated by studies of homophily and opinion formation, we propose a variant of snowball sampling designed to prioritize the inclusion of entire cohesive communities rather than any kind of representativeness, breadth, or depth of coverage. The method is illustrated on a concrete example, and experiments on synthetic networks suggest that it behaves as desired.

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Section: Sampling Social Media Networkmentioning

confidence: 99%

Tight Sampling in Unbounded Networks

Jaglan,

Pindiprolu,

Sharma

et al. 2024

ICWSM

View full text Add to dashboard Cite

show abstract

“…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. It uses a breadth-first search to obtain a set of unique nodes from a list of jump nodes.…”

Section: Graph Samplingmentioning

confidence: 99%

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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“…Hybrid Jump (HJ): HJ (Liu et al 2019) introduces a hybrid jump strategy into Metropolis-Hasting Random Walk during the sampling process. It uses a breadthfirst search to obtain a set of unique nodes from a list of jump nodes.…”

Section: Graph Samplingmentioning

confidence: 99%

Empirical Characterization of Graph Sampling Algorithms

Yousuf¹,

Anwer²,

Anwar³

2021

Preprint

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

A Novel Hybrid-Jump-Based Sampling Method for Complex Social Networks

Cited by 12 publications

References 32 publications

Tight Sampling in Unbounded Networks

Tight Sampling in Unbounded Networks

Empirical Characterization of Graph Sampling Algorithms

Empirical Characterization of Graph Sampling Algorithms

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