Measuring social capital through network analysis and its influence on individual performance

Abbasi, Alireza; Wigand, Rolf T.; Hossain, Liaquat

doi:10.1016/j.lisr.2013.08.001

Cited by 79 publications

(56 citation statements)

References 37 publications

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“…A research collaboration can take various forms depending on the institution, field, sector and country, and is typically measured through multi-author or multi-address papers (Katz and Martin, 1997). Its importance stems from the benefits that it provides to academics, as it is associated with high academic performance and productivity (Abbasi et al, 2014;Ductor, 2014;Rostan and Ceravolo, 2015;Zutshi et al, 2012). A collaboration is usually initiated by the material, knowledge-based or social needs of academics, such as the need for infrastructure, research equipment and personnel (Melin, 2000;Rostan and Ceravolo, 2015).…”

Section: Engagement and Collaboration Within Academia And The Role Ofmentioning

confidence: 99%

Academic engagement: Differences between intention to adopt Social Networking Sites and other online technologies

Dermentzi

Papagiannidis

Osório³

et al. 2016

Computers in Human Behavior

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Section: Engagement and Collaboration Within Academia And The Role Ofmentioning

confidence: 99%

Academic engagement: Differences between intention to adopt Social Networking Sites and other online technologies

Dermentzi

Papagiannidis

Osório³

et al. 2016

Computers in Human Behavior

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“…Yan and Ding [21] analyzed the Library and Information Science co-authorship network in relation to the impact of their researchers, finding important correlations. Abbasi et al extensively studied the relationship between scientific impact and co-authorship pattern, discovering significant correlations between network indicators (density and egobetweenness) and performance indicators such as g-index [1] and citation counts [2]. McCarty et al [17] attempted to predict the h-index evolution through ego networks, observing that this factor increases if one can choose to coauthor articles with authors already having a high h-index.…”

Section: Related Workmentioning

confidence: 99%

On the Formation of Circles in Co-authorship Networks

Chakraborty

Patranabis

Goyal

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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The availability of an overwhelmingly large amount of bibliographic information including citation and co-authorship data makes it imperative to have a systematic approach that will enable an author to organize her own personal academic network profitably. An effective method could be to have one's co-authorship network arranged into a set of "circles", which has been a recent practice for organizing relationships (e.g., friendship) in many online social networks.In this paper, we propose an unsupervised approach to automatically detect circles in an ego network such that each circle represents a densely knit community of researchers. Our model is an unsupervised method which combines a variety of node features and node similarity measures. The model is built from a rich co-authorship network data of more than 8 hundred thousand authors. In the first level of evaluation, our model achieves 13.33% improvement in terms of overlapping modularity compared to the best among four state-of-the-art community detection methods. Further, we conduct a task-based evaluation -two basic frameworks for collaboration prediction are considered with the circle information (obtained from our model) included in the feature set. Experimental results show that including the circle information detected by our model improves the prediction performance by 9.87% and 15.25% on average in terms of AU C (Area under the ROC) and P rec@20 (Precision at Top 20) respectively compared to the case, where the circle information is not present.

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“…Abbasi et al [29] in their attempt to measure social capital proposed a set of metrices as assets in networks. More specifically they proposed:…”

Section: Related Workmentioning

confidence: 99%

Software Project Team Selection Based on Enterprise Social Networks

Fitsilis

Gerogiannis

Ανθόπουλος

2015

Lecture Notes in Electrical Engineering

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Abstract. Project management is a well understood management method, widely adopted today, in order to give predictable results to complex problems. It is based on the assumption that unique undertakings require flexible organizational structures and different skill sets, in order to be implemented successfully. It is evident that matching the required, by each project, skills with the skills offered from the available human capital, is not a trivial process. This need of building high performing, effective teams with the appropriate skill set, is quite important since it is a prerequisite for successful project completion. Furthermore the recent shift to agile project management, together with the fact that projects today do not only require technical skills but also behavioral and contextual skills increases the importance of project team selection. In this paper, we present the ONSOCIAL system, a system that attempts to capitalize knowledge accumulated over social networks and to support the project team selection process. For achieving this objective, the ONSOCIAL system is using information stored in social network systems, analyses the stored profiles and offers recommendations for candidate team members.

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Measuring social capital through network analysis and its influence on individual performance

Cited by 79 publications

References 37 publications

Academic engagement: Differences between intention to adopt Social Networking Sites and other online technologies

Academic engagement: Differences between intention to adopt Social Networking Sites and other online technologies

On the Formation of Circles in Co-authorship Networks

Software Project Team Selection Based on Enterprise Social Networks

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