Hiper-redes para análise de relações de coautoria
Resumo
Redes complexas são uma poderosa ferramenta para a compreensão de fenômenos nos mais diversos contextos. Entretanto, a modelagem de redes como grafos, por ser centrada em relações em pares, oferece limitações na modelagem de interações muitos-para-muitos, como é o caso da colaboração em artigos científicos. Este trabalho traz uma visão geral de conceitos centrais para a utilização de hiper-redes como modelos para a representação de relações sociais, discutindo vantagens e desvantagens, desafios e oportunidades. A comparação de modelos de redes e hiper-redes construídos sobre a CSBCSet, uma base de dados de artigos científicos publicados no CSBC, permite explorar o impacto no uso de hiper-redes para o estudo do fenômeno de coautoria de artigos científicos.Referências
Aksoy, S., Joslyn, C., Ortiz Marrero, C., Praggastis, B., and Purvine, E. (2020). Hypernetwork science via high-order hypergraph walks. EPJ Data Science, 9.
Antelmi, A. (2021). Beyond Pairwise Relationships: Modeling Real-world Dynamics Via High-order Networks. Phd thesis, Università degli Studi di Salerno, Salerno, Italy.
Barabási, A.-L. and Pósfai, M. (2016). Network science. Cambridge University Press, Cambridge.
Barrat, A., Barthélemy, M., Pastor-Satorras, R., and Vespignani, A. (2004). The architecture of complex weighted networks. PNAS, 101(11):3747–3752.
Barthélemy, M., Barrat, A., Pastor-Satorras, R., and Vespignani, A. (2005). Characterization and modeling of weighted networks. Physica A: Statistical Mechanics and its Applications, 346(1):34–43.
Battiston, F., Cencetti, G., Iacopini, I., Latora, V., Lucas, M., Patania, A., Young, J.-G., and Petri, G. (2020). Networks beyond pairwise interactions: Structure and dynamics. Physics Reports, 874:1–92.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008.
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., and Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1):1–122.
Bretto, A. (2013). Hypergraph Theory: An Introduction. Springer Publishing Company.
Bulò, S. and Pelillo, M. (2009). A game-theoretic approach to hypergraph clustering. In Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C., and Culotta, A., editors, Advances in Neural Information Processing Systems, volume 22. Curran.
Filho, S. L., Carvalho, L., Suzano, J., Brandão, M., Oliveira, J., and Santoro, F. (2023). Csbcset: Um conjunto de dados para uma década de csbc, seus eventos e publicações. In Anais do XII Brazilian Workshop on Social Network Analysis and Mining, pages 240–245, Porto Alegre, RS, Brasil. SBC.
Holme, P. and Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3):97–125. Temporal Networks.
Joslyn, C. A., Aksoy, S. G., Callahan, T. J., Hunter, L. E., Jefferson, B., Praggastis, B., Purvine, E., and Tripodi, I. J. (2021). Hypernetwork science: From multidimensional networks to computational topology. In Unifying Themes in Complex Systems X, pages 377–392, Cham. Springer International Publishing.
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., and Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3):203–271.
Kumar, T., Vaidyanathan, S., Ananthapadmanabhan, H., Parthasarathy, S., and Ravindran, B. (2018). Hypergraph clustering: A modularity maximization approach.
Lerner, J. and Hâncean, M.-G. (2023). Micro-level network dynamics of scientific collaboration and impact: Relational hyperevent models for the analysis of coauthor networks. Network Science, 11(1):5–35.
McDaid, A., Greene, D., and Hurley, N. (2011). Normalized mutual information to evaluate overlapping community finding algorithms. CoRR.
Newman, M. (2000). Who is the best connected scientist? A study of scientific coauthorship networks. Santa Fe Institute, Working Papers, 650.
Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl 1):5200–5205.
Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582.
Patania, A., Petri, G., and Vaccarino, F. (2017). The shape of collaborations. EPJ Data Science, 6:18.
Ullah, M., Shahid, A., Din, I., A., M., Assam, M., Fayaz, M., Ghadi, Y., and Aljuaid, H. (2022). Analyzing interdisciplinary research using co-authorship networks. Complexity.
Velden, T., Haque, A.-u., and Lagoze, C. (2009). A new approach to analyzing patterns of collaboration in co-authorship networks mesoscopic analysis and interpretation. Scientometrics, 85.
Vieira, V. d. F., Ferreira, C. H. G., Almeida, J. M., Moreira, E., Laender, A. H. F., Meira, W., and Gonçalves, M. A. (2024). A network-driven study of hyperprolific authors in computer science. Scientometrics.
Webber, W., Moffat, A., and Zobel, J. (2010). A similarity measure for indefinite rankings. ACM Trans. Inf. Syst., 28(4).
Zhou, D., Huang, J., and Schölkopf, B. (2006). Learning with hypergraphs: Clustering, classification, and embedding. In Schölkopf, B., Platt, J., and Hoffman, T., editors, Advances in Neural Information Processing Systems, volume 19. MIT Press.
Antelmi, A. (2021). Beyond Pairwise Relationships: Modeling Real-world Dynamics Via High-order Networks. Phd thesis, Università degli Studi di Salerno, Salerno, Italy.
Barabási, A.-L. and Pósfai, M. (2016). Network science. Cambridge University Press, Cambridge.
Barrat, A., Barthélemy, M., Pastor-Satorras, R., and Vespignani, A. (2004). The architecture of complex weighted networks. PNAS, 101(11):3747–3752.
Barthélemy, M., Barrat, A., Pastor-Satorras, R., and Vespignani, A. (2005). Characterization and modeling of weighted networks. Physica A: Statistical Mechanics and its Applications, 346(1):34–43.
Battiston, F., Cencetti, G., Iacopini, I., Latora, V., Lucas, M., Patania, A., Young, J.-G., and Petri, G. (2020). Networks beyond pairwise interactions: Structure and dynamics. Physics Reports, 874:1–92.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008.
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., and Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1):1–122.
Bretto, A. (2013). Hypergraph Theory: An Introduction. Springer Publishing Company.
Bulò, S. and Pelillo, M. (2009). A game-theoretic approach to hypergraph clustering. In Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C., and Culotta, A., editors, Advances in Neural Information Processing Systems, volume 22. Curran.
Filho, S. L., Carvalho, L., Suzano, J., Brandão, M., Oliveira, J., and Santoro, F. (2023). Csbcset: Um conjunto de dados para uma década de csbc, seus eventos e publicações. In Anais do XII Brazilian Workshop on Social Network Analysis and Mining, pages 240–245, Porto Alegre, RS, Brasil. SBC.
Holme, P. and Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3):97–125. Temporal Networks.
Joslyn, C. A., Aksoy, S. G., Callahan, T. J., Hunter, L. E., Jefferson, B., Praggastis, B., Purvine, E., and Tripodi, I. J. (2021). Hypernetwork science: From multidimensional networks to computational topology. In Unifying Themes in Complex Systems X, pages 377–392, Cham. Springer International Publishing.
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., and Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3):203–271.
Kumar, T., Vaidyanathan, S., Ananthapadmanabhan, H., Parthasarathy, S., and Ravindran, B. (2018). Hypergraph clustering: A modularity maximization approach.
Lerner, J. and Hâncean, M.-G. (2023). Micro-level network dynamics of scientific collaboration and impact: Relational hyperevent models for the analysis of coauthor networks. Network Science, 11(1):5–35.
McDaid, A., Greene, D., and Hurley, N. (2011). Normalized mutual information to evaluate overlapping community finding algorithms. CoRR.
Newman, M. (2000). Who is the best connected scientist? A study of scientific coauthorship networks. Santa Fe Institute, Working Papers, 650.
Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl 1):5200–5205.
Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582.
Patania, A., Petri, G., and Vaccarino, F. (2017). The shape of collaborations. EPJ Data Science, 6:18.
Ullah, M., Shahid, A., Din, I., A., M., Assam, M., Fayaz, M., Ghadi, Y., and Aljuaid, H. (2022). Analyzing interdisciplinary research using co-authorship networks. Complexity.
Velden, T., Haque, A.-u., and Lagoze, C. (2009). A new approach to analyzing patterns of collaboration in co-authorship networks mesoscopic analysis and interpretation. Scientometrics, 85.
Vieira, V. d. F., Ferreira, C. H. G., Almeida, J. M., Moreira, E., Laender, A. H. F., Meira, W., and Gonçalves, M. A. (2024). A network-driven study of hyperprolific authors in computer science. Scientometrics.
Webber, W., Moffat, A., and Zobel, J. (2010). A similarity measure for indefinite rankings. ACM Trans. Inf. Syst., 28(4).
Zhou, D., Huang, J., and Schölkopf, B. (2006). Learning with hypergraphs: Clustering, classification, and embedding. In Schölkopf, B., Platt, J., and Hoffman, T., editors, Advances in Neural Information Processing Systems, volume 19. MIT Press.
Publicado
21/07/2024
Como Citar
SANTOS, Matheus H. B. dos; ALMEIDA, Jussara M. de; XAVIER, Carolina R.; VIEIRA, Vinícius da F..
Hiper-redes para análise de relações de coautoria. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 13. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 172-185.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2024.3124.