Synthetic generation of social network data with endorsements

Pérez-Rosés, Hebert; Sebé, Francesc

doi:10.1057/jos.2014.29

Cited by 12 publications

(10 citation statements)

References 18 publications

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“…Many studies carried out to generate synthetic data for SN. In [71], a synthetic SN users' skills data was generated for specific types of SN, like LinkedIn and ResearchGate, and the network growth was simulated based on the skills' endorsement scores. In [72], synthetic data was generated and populating to SN topology.…”

Section: Social Network Synthetic Graph Generationmentioning

confidence: 99%

Online Social Networks (OSN) Evolution Model Based on Homophily and Preferential Attachment

Khan

Lee

2018

Symmetry

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In this paper, we propose a new scale-free social networks (SNs) evolution model that is based on homophily combined with preferential attachments. Our model enables the SN researchers to generate SN synthetic data for the evaluation of multi-facet SN models that are dependent on users’ attributes and similarities. Homophily is one of the key factors for interactive relationship formation in SN. The synthetic graph generated by our model is scale-invariant and has symmetric relationships. The model is dynamic and sustainable to changes in input parameters, such as number of nodes and nodes’ attributes, by conserving its structural properties. Simulation and evaluation of models for large-scale SN applications need large datasets. One way to get SN data is to generate synthetic data by using SN evolution models. Various SN evolution models are proposed to approximate the real-life SN graphs in previous research. These models are based on SN structural properties such as preferential attachment. The data generated by these models is suitable to evaluate SN models that are structure dependent but not suitable to evaluate models which depend on the SN users’ attributes and similarities. In our proposed model, users’ attributes and similarities are utilized to synthesize SN graphs. We evaluated the resultant synthetic graph by analyzing its structural properties. In addition, we validated our model by comparing its measures with the publicly available real-life SN datasets and previous SN evolution models. Simulation results show our resultant graph to be a close representation of real-life SN graphs with users’ attributes.

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Section: Social Network Synthetic Graph Generationmentioning

confidence: 99%

Online Social Networks (OSN) Evolution Model Based on Homophily and Preferential Attachment

Khan

Lee

2018

Symmetry

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“…More precisely, we discuss the benefits of endorsement deduction in terms of (1) consistency with the results of simple weighted PageRank, (2) reduction in the number of ties and (3) robustness against spamming. Following this methodology, we test our solution on a synthetic network of 1493 nodes and 2489 edges, similar to LinkedIn, and fitted with endorsements [39].…”

Section: Contribution and Plan Of This Papermentioning

confidence: 99%

Endorsement deduction and ranking in social networks

Pérez-Rosés

Sebé

Ribó

2016

Computer Communications

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Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements.

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“…Other studies have also attempted to explore data such as the endorsements and profiles from social networking websites. However, the focus on endorsements includes proposing new methods to compute artificial data (Pérez-Rosés and Sebé, 2015), and improve data consistency (Pérez-Rosés and Sebé, 2016). Also, several studies focused on exploring social networking profiles to provide recommendations for improving employability (Zide and Shanani-Denning, 2014;Chiang and Suen, 2015).…”

Section: Introductionmentioning

confidence: 99%

Building Information Modelling Skills in Construction Related Disciplines: A Social Network and Job Advertisement-Based Comparative Analysis

Rahman¹,

Ayer²,

Tang³

et al. 2017

EPOJ

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Previous research efforts have attempted to identify the necessary Building Information Modelling (BIM) skills for individuals. However, prior works did not explore those needed for entry-level positions, and may be susceptible to respondents’ bias. This study proposes using methods that do not require individual responses to identify those skills that are related to BIM. Specifically, the authors suggest using publicly available data on social networking profiles. This paper presents the results of the authors’ research on identifying skills that are related to BIM and how they might be used in the industry. This study identifies those skills for graduating students by analysing social networking profiles and entry-level job advertisements. Additionally, the job tasks in those advertisements are analysed to illustrate how the skills might be used. This study performs those analyses to answer the following: (1) How do the skills differ between those that are possessed by individuals related to BIM and those that are listed in entry-level job advertisements that require BIM? And (2) How do the job tasks differ between BIM-specialized and nonBIM-specialized positions from the same entry-level job advertisements? The results suggest that there are discrepancies between the skills identified from the profiles and the advertisements. Additionally, while BIM skills may be a standard expectation in all positions in the future, there are BIM tasks that may disappear when BIM reaches maturity in the industry. Identifying and prioritizing the critical skills based on this type of analysis may enable educators to better prepare students for long-term success.

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Synthetic generation of social network data with endorsements

Cited by 12 publications

References 18 publications

Online Social Networks (OSN) Evolution Model Based on Homophily and Preferential Attachment

Online Social Networks (OSN) Evolution Model Based on Homophily and Preferential Attachment

Endorsement deduction and ranking in social networks

Building Information Modelling Skills in Construction Related Disciplines: A Social Network and Job Advertisement-Based Comparative Analysis

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