Individual-Level Social Capital in Weighted and Attributed Social Networks

Sharma, Rajesh; McAreavey, Kevin; Hong, Jun; Ghaffar, Faisal

doi:10.1109/asonam.2018.8508302

Cited by 3 publications

(5 citation statements)

References 29 publications

(43 reference statements)

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“…We will also draw on work on bonding and bridging measurements as social capital [10] and collaborative ratio measurement [15]. These metrics are as inputs to our machine learning scheme in Section 4.3.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Using Machine Learning to Predict Links and Improve Steiner Tree Solutions to Team Formation Problems

Keane

Ghaffar²,

Malone

2019

Complex Networks and Their Applications VIII

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The team formation problem has existed for many years in various guises. One important problem in the team formation problem is to produce small teams that have a required set of skills. We propose a framework that incorporates machine learning to predict unobserved links between collaborators, alongside improved Steiner tree problems to form small teams to cover given tasks. Our framework not only considers size of the team but also how likely are team members going to collaborate with each other. The results show that this model consistently returns smaller collaborative teams.

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Section: Related Workmentioning

confidence: 99%

“…The bonding and bridging capital [10] of each inventor was calculated by getting the communities of the graph, using the best partition function in networkx's community module, and seeing how many of the neighbors of each inventor are in the same community. The bonding capital is given by:…”

Section: Machine Learning Model Trainingmentioning

confidence: 99%

Using Machine Learning to Predict Links and Improve Steiner Tree Solutions to Team Formation Problems

Keane

Ghaffar²,

Malone

2019

Complex Networks and Their Applications VIII

Self Cite

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“…In addition, we will also make use of a number of metrics specifically designed, including bonding capital and bridging capital, intended for use in collaboration networks. Such metrics include social capital (Sharma et al 2018) and collaborative ratio measurement (Wu et al 2013). The metrics we use are described in more detail in "Machine learning model training" section.…”

Section: Related Workmentioning

confidence: 99%

“…The bonding and bridging capital (Sharma et al 2018) of each inventor are designed to measure homogenity and heterogenity of ties of inventors. They are calculated as follows.…”

Section: Machine Learning Model Trainingmentioning

confidence: 99%

Using machine learning to predict links and improve Steiner tree solutions to team formation problems - a cross company study

Keane

Ghaffar²,

Malone

2020

Appl Netw Sci

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The team formation problem has existed for many years in various guises. One important challenge in the team formation problem is to produce small teams that have a required set of skills. We propose a framework that incorporates machine learning to augment a collaboration graph with latent links between collaborators. This is combined with the solution of Steiner tree problems to form small teams that cover a specified set of tasks. Our framework not only considers the size of the team but also the likelihood that team members are going to collaborate with each other. We demonstrate our results using data from the US Patent office covering two different companies' inventor networks. The results show that this technique can reduce the size of suggested teams.

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“…Subbian et al [6] took a distance based approach, where direct connections were considered as bonding capital and indirect connections as bridging capital. The series of works by Smith et al [7,8] and Sharma et al [9] focused on characteristics, for example Smith et al making use of blog.…”

Section: Introductionmentioning

confidence: 99%

Community-Based Measures for Social Capital

Spratt¹,

Hong²,

McAreavey³

et al. 2018

Studies in Computational Intelligence

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Social capital is the value that can be derived from connections between individuals in a social network. The most common forms are bonding and bridging social capital, resulting from connections with similar and diverse groups of individuals respectively. In this paper we propose a novel communitybased model for measuring bonding and bridging social capital in a social network. Some previous measures of bonding and bridging capital depend on node attributes, which are often difficult to obtain. Other measures overcome this limitation by relying purely on network structure but are limited to direct connections only for bonding capital and indirect connections only for bridging capital. Our structural measures for bonding and bridging capital are independent of attributes and account for both direct and indirect connections. We experimentally validate our measures on a collaboration network extracted from DBLP, and our results show a strong correlation with standard measures of academic success.

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Individual-Level Social Capital in Weighted and Attributed Social Networks

Cited by 3 publications

References 29 publications

Using Machine Learning to Predict Links and Improve Steiner Tree Solutions to Team Formation Problems

Using Machine Learning to Predict Links and Improve Steiner Tree Solutions to Team Formation Problems

Using machine learning to predict links and improve Steiner tree solutions to team formation problems - a cross company study

Community-Based Measures for Social Capital

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