Potencial de influência em publicações usando território causal
Resumo
Grafos temporais são uma extensão dos grafos convencionais para representar eventos que ocorrem no tempo. Neste trabalho introduzimos um algoritmo que calcula o conjunto de membros de uma rede que são influenciados direta ou indiretamente com custo computacional O(V C), tal qual encontrado na literatura, mas com média menor. Formalmente definimos o conceito de Território Causal. Realizamos experimentos sobre os primeiros cinco anos de carreira dos pesquisadores presentes na DBLP. Os principais resultados indicam que: (i) o tamanho dos territórios causais vem aumentando; (ii) a concentração da quantidade de pesquisadores potencialmente influenciados vem se estabilizando; e (iii) os pesquisadores tem aumentado o seu alcance de influência.Referências
Barros, C. D. T., Mendonça, M. R. F., Vieira, A. B., and Ziviani, A. (2021). A survey on embedding dynamic graphs. ACM Comput. Surv., 55(1).
Dijkstra, E. W. (1959). A Note on Two Problems in Connexion with Graphs, page 287290. Association for Computing Machinery, New York, NY, USA, 1 edition.
Greene, D., Doyle, D., and Cunningham, P. (2010). Tracking the evolution of communities in dynamic social networks. In 2010 International Conference on Advances in Social Networks Analysis and Mining, pages 176–183.
Holme, P. (2014). Analyzing temporal networks in social media. Proceedings of the IEEE, 102(12):1922–1933.
Holme, P. and Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3):97–125.
Katz, J. and Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1):1–18.
Kong, X., Shi, Y., Yu, S., Liu, J., and Xia, F. (2019). Academic social networks: Modeling, analysis, mining and applications. Journal of Network and Computer Applications, 132:86–103.
Ley, M. (2002). The dblp computer science bibliography: Evolution, research issues, perspectives. In Laender, A. H. F. and Oliveira, A. L., editors, String Processing and Information Retrieval, pages 1–10, Berlin, Heidelberg. Springer Berlin Heidelberg.
Michail, O. (2016). An introduction to temporal graphs: An algorithmic perspective. Internet Mathematics, 12(4):239–280.
Nane, G. F., Larivière, V., and Costas, R. (2017). Predicting the age of researchers using bibliometric data. Journal of Informetrics, 11(3):713–729.
Palla, G., Barabasi, A.-L., and Vicsek, T. (2007). Quantifying social group evolution. Nature, 446(7136):664–667.
Tang, J., Musolesi, M., Mascolo, C., Latora, V., and Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. In 3rd Workshop on Social Network Systems.
Wu, H., Cheng, J., Huang, S., Ke, Y., Lu, Y., and Xu, Y. (2014). Path problems in temporal graphs. In Proceedings of the Very Large Databases Endowment, volume 7, pages 721–732. VLDB Endowment.
Yu, Z., Du, R., Guo, B., Xu, H., Gu, T., Wang, Z., and Zhang, D. (2015). Who should i invite for my party? combining user preference and influence maximization for social events. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp ’15, page 879883, New York, NY, USA. Association for Computing Machinery.
Dijkstra, E. W. (1959). A Note on Two Problems in Connexion with Graphs, page 287290. Association for Computing Machinery, New York, NY, USA, 1 edition.
Greene, D., Doyle, D., and Cunningham, P. (2010). Tracking the evolution of communities in dynamic social networks. In 2010 International Conference on Advances in Social Networks Analysis and Mining, pages 176–183.
Holme, P. (2014). Analyzing temporal networks in social media. Proceedings of the IEEE, 102(12):1922–1933.
Holme, P. and Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3):97–125.
Katz, J. and Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1):1–18.
Kong, X., Shi, Y., Yu, S., Liu, J., and Xia, F. (2019). Academic social networks: Modeling, analysis, mining and applications. Journal of Network and Computer Applications, 132:86–103.
Ley, M. (2002). The dblp computer science bibliography: Evolution, research issues, perspectives. In Laender, A. H. F. and Oliveira, A. L., editors, String Processing and Information Retrieval, pages 1–10, Berlin, Heidelberg. Springer Berlin Heidelberg.
Michail, O. (2016). An introduction to temporal graphs: An algorithmic perspective. Internet Mathematics, 12(4):239–280.
Nane, G. F., Larivière, V., and Costas, R. (2017). Predicting the age of researchers using bibliometric data. Journal of Informetrics, 11(3):713–729.
Palla, G., Barabasi, A.-L., and Vicsek, T. (2007). Quantifying social group evolution. Nature, 446(7136):664–667.
Tang, J., Musolesi, M., Mascolo, C., Latora, V., and Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. In 3rd Workshop on Social Network Systems.
Wu, H., Cheng, J., Huang, S., Ke, Y., Lu, Y., and Xu, Y. (2014). Path problems in temporal graphs. In Proceedings of the Very Large Databases Endowment, volume 7, pages 721–732. VLDB Endowment.
Yu, Z., Du, R., Guo, B., Xu, H., Gu, T., Wang, Z., and Zhang, D. (2015). Who should i invite for my party? combining user preference and influence maximization for social events. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp ’15, page 879883, New York, NY, USA. Association for Computing Machinery.
Publicado
06/08/2023
Como Citar
RAMOS, Diogo F. S.; MENA-CHALCO, Jesús P..
Potencial de influência em publicações usando território causal. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 12. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 187-197.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2023.230902.
