Uma Abordagem para Classificação de Interações Sociais Dinâmicas a partir de seus Atributos
Resumo
Network analyses provide important information for understanding how a network evolves. In this context, some studies focus on classifying nodes and their relationships based on topological properties and centrality metrics. Instead, we discuss the importance of applying the notion of social capital to the classification process. Here, we propose a new approach to classify nodes and edges in temporal multigraphs based on the persistence of the edges’ attributes. Overall, our results show that the social role of the nodes and the strength of their ties are statistically well-defined when compared with several traditional graph metrics.
Palavras-chave:
Edge Classification, Node Classification, Social Networks
Referências
Adamic, L. A. and Adar, E. Friends and neighbors on the web. Social Networks 25 (3): 211–230, 2003.
Alves, B. L., Benevenuto, F., and Laender, A. H. F. The Role of Research Leaders on the Evolution of Scientific Communities. In Proc. of the 22nd Int’l Conf. on the World Wide Web (Comp. Volume). pp. 649–656, 2013.
Barabási, A.-L. Scale-free networks: a decade and beyond. Science 325 (5939): 412–413, 2009.
Brandão, M. A. and Moro, M. M. The strength of co-authorship ties through different topological properties. Journal of the Brazilian Computer Society 23 (1): 5, 2017.
Burt, R. S. Brokerage and closure: An introduction to social capital. Oxford University Press, 2005.
Easley, D. and Kleinberg, J. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York, NY, USA, 2010.
Freire, V. P. and Figueiredo, D. R. Ranking in collaboration networks using a group based metric. Journal of the Brazilian Computer Society 17 (4): 255–266, Nov, 2011.
Gilbert, E. and Karahalios, K. Predicting tie strength with social media. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems. pp. 211–220, 2009.
Granovetter, M. S. The Strength of Weak Ties. American Journal of Sociology 78 (6): 1360–1380, 1973.
Hollander, M., Wolfe, D. A., and Chicken, E. Nonparametric statistical methods. Vol. 751. John Wiley & Sons, 2013.
Leão, J. C., Brandão, M. A., Vaz de Melo, P., and Laender, A. H. F. Who is really in my social circle? Mining Social Relationships to Improve Detection of Real Communities (to appear). Journal of Internet Services and Applications, 2018.
Newman, M. E. pp. 337–370. In , Who Is the Best Connected Scientist? A Study of Scientific Coauthorship Networks. Springer Berlin Heidelberg, pp. 337–370, 2004.
Shah, N., Beutel, A., Hooi, B., Akoglu, L., Gunnemann, S., Makhija, D., Kumar, M., and Faloutsos, C. EdgeCentric: Anomaly Detection in Edge-Attributed Networks. In In Proc. of the IEEE 16th International Conference on Data Mining Workshops. pp. 327–334, 2016.
Silva, T. H. P., Rocha, L. M., Silva, A. P. C., and Moro, M. M. 3c-index: Research Contribution across Communities as an Influence Indicator. Journal of Information and Data Management 6 (3): 192, 2015.
Vaz de Melo, P. O. S., Viana, A. C., Fiore, M., Jaffrès-Runser, K., Mouël, F. L., Loureiro, A. A. F., Addepalli, L., and Chen, G. RECAST: Telling Apart Social and Random Relationships in Dynamic Networks. Perform. Eval. vol. 87, pp. 19–36, 2015.
Watts, D. J. The “New” Science of Networks. Annual Review of Sociology vol. 30, pp. 243–270, 2004.
Alves, B. L., Benevenuto, F., and Laender, A. H. F. The Role of Research Leaders on the Evolution of Scientific Communities. In Proc. of the 22nd Int’l Conf. on the World Wide Web (Comp. Volume). pp. 649–656, 2013.
Barabási, A.-L. Scale-free networks: a decade and beyond. Science 325 (5939): 412–413, 2009.
Brandão, M. A. and Moro, M. M. The strength of co-authorship ties through different topological properties. Journal of the Brazilian Computer Society 23 (1): 5, 2017.
Burt, R. S. Brokerage and closure: An introduction to social capital. Oxford University Press, 2005.
Easley, D. and Kleinberg, J. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York, NY, USA, 2010.
Freire, V. P. and Figueiredo, D. R. Ranking in collaboration networks using a group based metric. Journal of the Brazilian Computer Society 17 (4): 255–266, Nov, 2011.
Gilbert, E. and Karahalios, K. Predicting tie strength with social media. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems. pp. 211–220, 2009.
Granovetter, M. S. The Strength of Weak Ties. American Journal of Sociology 78 (6): 1360–1380, 1973.
Hollander, M., Wolfe, D. A., and Chicken, E. Nonparametric statistical methods. Vol. 751. John Wiley & Sons, 2013.
Leão, J. C., Brandão, M. A., Vaz de Melo, P., and Laender, A. H. F. Who is really in my social circle? Mining Social Relationships to Improve Detection of Real Communities (to appear). Journal of Internet Services and Applications, 2018.
Newman, M. E. pp. 337–370. In , Who Is the Best Connected Scientist? A Study of Scientific Coauthorship Networks. Springer Berlin Heidelberg, pp. 337–370, 2004.
Shah, N., Beutel, A., Hooi, B., Akoglu, L., Gunnemann, S., Makhija, D., Kumar, M., and Faloutsos, C. EdgeCentric: Anomaly Detection in Edge-Attributed Networks. In In Proc. of the IEEE 16th International Conference on Data Mining Workshops. pp. 327–334, 2016.
Silva, T. H. P., Rocha, L. M., Silva, A. P. C., and Moro, M. M. 3c-index: Research Contribution across Communities as an Influence Indicator. Journal of Information and Data Management 6 (3): 192, 2015.
Vaz de Melo, P. O. S., Viana, A. C., Fiore, M., Jaffrès-Runser, K., Mouël, F. L., Loureiro, A. A. F., Addepalli, L., and Chen, G. RECAST: Telling Apart Social and Random Relationships in Dynamic Networks. Perform. Eval. vol. 87, pp. 19–36, 2015.
Watts, D. J. The “New” Science of Networks. Annual Review of Sociology vol. 30, pp. 243–270, 2004.
Publicado
22/10/2018
Como Citar
SILVA, Thiago H. P.; LAENDER, Alberto H. F..
Uma Abordagem para Classificação de Interações Sociais Dinâmicas a partir de seus Atributos. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 6. , 2018, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 57-64.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2018.27385.
