1 |
Barabási, A. and Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439):509–512.
|
|
2 |
Bartusiak, R. et al. (2016). Cooperation prediction in github developers network with restricted boltzmann machine. In ACIIDS, pages 96–107.
|
|
3 |
Brandão, M., Moro, M. M., and Almeida, J. M. (2013). Análise de fatores impactantes na recomendação de colaborações acadêmicas utilizando projeto fatorial. In SBBD Short Papers, pages 1–6.
|
|
4 |
Brandão, M. A., Diniz, M. A., and Moro, M. M. (2016). Using topological properties to measure the strength of co-authorship ties. In BRASNAM/CSBC, pages 199–210.
|
|
5 |
Brandão, M. A., Moro, M. M., and Almeida, J. M. (2014). Experimental evaluation of academic collaboration recommendation using factorial design. JIDM, 5(1):52.
|
|
6 |
Casalnuovo, C., Vasilescu, B., Devanbu, P., and Filkov, V. (2015). Developer onboarding in github: The role of prior social links and language experience. In ESEC/FSE, pages 817–828.
|
|
7 |
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, second edition.
|
|
8 |
Dabbish, L., Stuart, C., Tsay, J., and Herbsleb, J. (2012). Social coding in github: transparency and collaboration in an open software repository. In CSCW, pages 1277–1286.
|
|
9 |
de Oliveira, D. M. et al. (2015). Uma estratégia não supervisionada para previsão de eventos usando redes sociais. In SBBD, pages 137–148.
|
|
10 |
Easley, D. and Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.
|
|
11 |
Gousios, G. (2013). The ghtorrent dataset and tool suite. In MSR, pages 233–236.
|
|
12 |
Newman, M. E. (2001). The structure of scientific collaboration networks. NAS, 98(2):404–409.
|
|
13 |
Silva, T. H. P., Rocha, L. M. A., Silva, A. P. C., and Moro, M. M. (2016). 3c-index: Research contribution across communities as an influence indicator. JIDM, 6(3):192–205.
|
|
14 |
Tsay, J., Dabbishand, L., and Herbsleb, J. (2014). Influence of social and technical factors for evaluating contribution in github. In ICSE, pages 356–366.
|
|