Using Topological Properties to Measure the Strength of Co-authorship Ties
Resumo
Studying the strength of ties in social networks allows to identify impact at micro-macro levels in the network, to analyze how distinct relationships play different roles, and so on. Indeed, the strength of ties has been investigated in many contexts with different goals. Here, we aim to address the problem of measuring ties strength in co-authorship social networks. Specifically, we present four case studies detailing problems with current metrics and propose a new one. Then, we build a co-authorship social network by using a real digital library and identify how the strength of ties relates to the quality of publication venues when measured by different topological properties. Our results show the best ranked venues have similar patterns of strength of co-authorship ties.
Palavras-chave:
Força de Relacionamentos, Redes Sociais de Coautoria, Propriedades Topológicas
Referências
Brandão, M. A. and Moro, M. M. (2015). Analyzing the strength of co-authorship ties with neighborhood overlap. In Procs. of DEXA, pages 527–542, Linz, Austria.
Burt, R. S. (2010). Neighbor networks: Competitive advantage local and personal. Oxford University Press.
Cheng et al., C.-B. (2014). Study of scientific collaborations in the intelligence and security informatics research community by social network analysis. In Procs. of CSBC–BraSNAM, Rio de janeiro, Brazil.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Second edition.
Cormen, T. H. (2009). Introduction to algorithms. MIT press.
Digiampietri, L. and Maruyama, W. (2014). Predição de novas coautorias na rede social acadêmica dos programas brasileiros de pós-graduação em ciência da computação. In Procs. of CSBC – BraSNAM, pages 243–248, Rio de janeiro, Brazil.
Ductor, L. (2015). Does co-authorship lead to higher academic productivity? Oxford Bulletin of Economics and Statistics, 77(3):385–407.
Easley, D. and Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.
Gilbert, E. and Karahalios, K. (2009). Predicting tie strength with social media. In Procs. of SIGCHI, pages 211–220, New York, USA.
Goulas et al., A. (2014). The strength of weak connections in the macaque cortico-cortical network. Brain Structure and Function, pages 1–13.
Granovetter, M. S. (1973). The strength of weak ties. The American Journal of Sociology, 78(6):1360–1380.
Jain, R. (1991). The Art of Computer Systems Performance Analysis: techniques for experimental design, measurement, simulation, and modeling. John Wiley & Sons.
Kahanda, I. and Neville, J. (2009). Using transactional information to predict link strength in online social networks. ICWSM, 9:74–81.
Laender et al, A. H. F. (2008). Assessing the research and education quality of the top brazilian computer science graduate programs. ACM SIGCSE Bulletin, 40(2):135–145.
Lewis, P. and McKenzie, E. (1988). Simulation methodology for statisticians, operations analysts, and engineers, volume 1. CRC press.
Machado, M., Andrade, R., and Serpa, R. (2013). Teambuilder: Uso de mídias sociais para a colaboração de grupos na organização de tarefas. In Procs. of CSBC – BraSNAM, Rio de janeiro, Brazil.
McGee, J., Caverlee, J. A., and Cheng, Z. (2011). A geographic study of tie strength in social media. In Procs. of CIKM, pages 2333–2336, New York, USA.
Pappalardo, L., Rossetti, G., and Pedreschi, D. (2012). How well do we know each other? detecting tie strength in multidimensional social networks. In Procs. of ASONAM, pages 1040–1045, Washington, USA.
Silva et al., T. H. P. (2014). Community-based endogamy as an influence indicator. In Procs. of JCDL, pages 67–76, Piscataway, USA.
Wiese et al., J. (2015). You never call, you never write: Call and sms logs do not always indicate tie strength. In Procs. of CSCW, pages 765–774, New York, USA.
Yan et al., R. (2012). To better stand on the shoulder of giants. In Procs. of JCDL, pages 51–60, New York, USA.
Zignani, M., Gaito, S., and Rossi, G. P. (2016). Predicting the link strength of “newborn” links. In Procs. of WWW, pages 147–148, Switzerland.
Burt, R. S. (2010). Neighbor networks: Competitive advantage local and personal. Oxford University Press.
Cheng et al., C.-B. (2014). Study of scientific collaborations in the intelligence and security informatics research community by social network analysis. In Procs. of CSBC–BraSNAM, Rio de janeiro, Brazil.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Second edition.
Cormen, T. H. (2009). Introduction to algorithms. MIT press.
Digiampietri, L. and Maruyama, W. (2014). Predição de novas coautorias na rede social acadêmica dos programas brasileiros de pós-graduação em ciência da computação. In Procs. of CSBC – BraSNAM, pages 243–248, Rio de janeiro, Brazil.
Ductor, L. (2015). Does co-authorship lead to higher academic productivity? Oxford Bulletin of Economics and Statistics, 77(3):385–407.
Easley, D. and Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.
Gilbert, E. and Karahalios, K. (2009). Predicting tie strength with social media. In Procs. of SIGCHI, pages 211–220, New York, USA.
Goulas et al., A. (2014). The strength of weak connections in the macaque cortico-cortical network. Brain Structure and Function, pages 1–13.
Granovetter, M. S. (1973). The strength of weak ties. The American Journal of Sociology, 78(6):1360–1380.
Jain, R. (1991). The Art of Computer Systems Performance Analysis: techniques for experimental design, measurement, simulation, and modeling. John Wiley & Sons.
Kahanda, I. and Neville, J. (2009). Using transactional information to predict link strength in online social networks. ICWSM, 9:74–81.
Laender et al, A. H. F. (2008). Assessing the research and education quality of the top brazilian computer science graduate programs. ACM SIGCSE Bulletin, 40(2):135–145.
Lewis, P. and McKenzie, E. (1988). Simulation methodology for statisticians, operations analysts, and engineers, volume 1. CRC press.
Machado, M., Andrade, R., and Serpa, R. (2013). Teambuilder: Uso de mídias sociais para a colaboração de grupos na organização de tarefas. In Procs. of CSBC – BraSNAM, Rio de janeiro, Brazil.
McGee, J., Caverlee, J. A., and Cheng, Z. (2011). A geographic study of tie strength in social media. In Procs. of CIKM, pages 2333–2336, New York, USA.
Pappalardo, L., Rossetti, G., and Pedreschi, D. (2012). How well do we know each other? detecting tie strength in multidimensional social networks. In Procs. of ASONAM, pages 1040–1045, Washington, USA.
Silva et al., T. H. P. (2014). Community-based endogamy as an influence indicator. In Procs. of JCDL, pages 67–76, Piscataway, USA.
Wiese et al., J. (2015). You never call, you never write: Call and sms logs do not always indicate tie strength. In Procs. of CSCW, pages 765–774, New York, USA.
Yan et al., R. (2012). To better stand on the shoulder of giants. In Procs. of JCDL, pages 51–60, New York, USA.
Zignani, M., Gaito, S., and Rossi, G. P. (2016). Predicting the link strength of “newborn” links. In Procs. of WWW, pages 147–148, Switzerland.
Publicado
05/07/2016
Como Citar
BRANDÃO, Michele A.; DINIZ, Matheus A.; MORO, Mirella M..
Using Topological Properties to Measure the Strength of Co-authorship Ties. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 2016. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 199-210.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2016.6455.
