Generating Graphs by Creating Associative and Random Links Between Existing Nodes

Yousuf, Muhammad Irfan; Kim, Suhyun

doi:10.1007/s10955-020-02517-z

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…A number of algorithms exist for generating various time-evolving networks, including social networks [25][26][27][28] . There are three papers [29][30][31] that present algorithms for generating time-evolving online social networks, each focusing on generating random networks under certain assumptions that are believed to hold true in online social networks. Yousuf and Kim 29,30 have presented algorithms that evolve a model based on the ideas of the rich-get-richer, socialization over time, and the transitive nature of relations between nodes (individuals) in a social network.…”

Section: Related Workmentioning

confidence: 99%

“…There are three papers [29][30][31] that present algorithms for generating time-evolving online social networks, each focusing on generating random networks under certain assumptions that are believed to hold true in online social networks. Yousuf and Kim 29,30 have presented algorithms that evolve a model based on the ideas of the rich-get-richer, socialization over time, and the transitive nature of relations between nodes (individuals) in a social network. The model starts with a closed triplet as the initial network and then adds a single node along with some number of edges using a local preferential attachment rule to create closed friendship triangles at every time step.…”

Section: Related Workmentioning

confidence: 99%

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A time evolving online social network generation algorithm

Shirzadian

Antony²,

Gattani³

et al. 2023

Sci Rep

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The rapid growth of online social media usage in our daily lives has increased the importance of analyzing the dynamics of online social networks. However, the dynamic data of existing online social media platforms are not readily accessible. Hence, there is a necessity to synthesize networks emulating those of online social media for further study. In this work, we propose an epidemiology-inspired and community-based, time-evolving online social network generation algorithm (EpiCNet), to generate a time-evolving sequence of random networks that closely mirror the characteristics of real-world online social networks. Variants of the algorithm can produce both undirected and directed networks to accommodate different user interaction paradigms. EpiCNet utilizes compartmental models inspired by mathematical epidemiology to simulate the flow of individuals into and out of the online social network. It also employs an overlapping community structure to enable more realistic connections between individuals in the network. Furthermore, EpiCNet evolves the community structure and connections in the simulated online social network as a function of time and with an emphasis on the behavior of individuals. EpiCNet is capable of simulating a variety of online social networks by adjusting a set of tunable parameters that specify the individual behavior and the evolution of communities over time. The experimental results show that the network properties of the synthetic time-evolving online social network generated by EpiCNet, such as clustering coefficient, node degree, and diameter, match those of typical real-world online social networks such as Facebook and Twitter.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A time evolving online social network generation algorithm

Shirzadian

Antony²,

Gattani³

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The parameter values of each generative model are tuned such that the generated network has nearly the same number of nodes and edges as that of the average of the twelve real-world datasets. The three generative models are: 1) Forest Fire (FF) [17] 2) Small World (SW) [18] and 3) Mixed Model (MM) [19]. We use sampling fractions φ = {0.02, 0.04, 0.06, 0.08, 0.1} to extract samples from these graphs.…”

Section: Datasetsmentioning

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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“…The parameter values of each generative model are tuned such that the generated network has nearly the same number of nodes and edges as that of the average of the twelve real-world datasets. The three generative models are: 1) Forest Fire (FF) (Leskovec et al 2007) 2) Small World (SW) (Watts and Strogatz 1998) and 3) Mixed Model (MM) (Yousuf and Kim 2020a). We use sampling fractions φ = {0.02, 0.04, 0.06, 0.08, 0.1} to extract samples from these graphs.…”

Section: Datasetsmentioning

confidence: 99%

Empirical Characterization of Graph Sampling Algorithms

Yousuf¹,

Anwer²,

Anwar³

2021

Preprint

Self Cite

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.

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Generating Graphs by Creating Associative and Random Links Between Existing Nodes

Cited by 5 publications

References 31 publications

A time evolving online social network generation algorithm

A time evolving online social network generation algorithm

Empirical Characterization of Graph Sampling Algorithms

Empirical Characterization of Graph Sampling Algorithms

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