Name
Abstract | ||
|---|---|---|
Sociological theories of career success provide fundamental principles for the analysis of social links to identify patterns that facilitate career development. Some theories (e.g. Granovetter's Strength of Weak Ties Theory and Burt's Structural Hole Theory) have shown that certain types of social ties provide career advantage to individuals by facilitating them to access unique information and connecting them with a diverse range of others in different social cliques. The assessment of link types and prediction of new links in the external social networks such as Facebook and Twitter have been studied extensively. However, this has not been addressed in the enterprise social networks and especially the prediction of weak ties in the context of employee career development. In this paper, we address this problem by proposing an Enterprise Weak Ties Recommendation (EWTR) framework which leverages enterprise social networks, employee collaboration activity streams and the organizational chart. We formulate weak ties recommendation as a link prediction problem. However, unlike any generic link prediction work, we first validated explicit enterprise social network with a set of heterogeneous collaboration networks and show assessment improves the explicit network's effectiveness in predicting new links. Furthermore, we leverage assessed social network for the weak ties prediction by optimizing the link prediction methods using organizational chart information. We demonstrate that optimization improves prediction accuracy in terms of AUC and average precision and our characterization of weak ties to a certain extent aligns with Granovetter's and Burt's seminal studies.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.5555/3382225.3382373 | ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining
Barcelona
Spain
August, 2018 |
Field | DocType | ISBN |
Sociological theory,Leverage (finance),Social network,Organizational chart,Computer science,Knowledge management,Artificial intelligence,Career development,Interpersonal ties,Machine learning | Conference | 978-1-5386-6051-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faisal Ghaffar | 1 | 0 | 1.69 |
| Teodora Sandra Buda | 2 | 26 | 7.50 |
| Haytham Assem | 3 | 21 | 6.00 |
| Armita Afsharinejad | 4 | 24 | 4.17 |
| Neil J. Hurley | 5 | 10 | 2.83 |