Utilizando aprendizado por representação para a classificação de laços sociais da IoT
Resumo
A Internet of Things (IoT) tem sido marcada pelas interações entre dispositivos que cooperam para realizar atividades. Este cenário viabiliza o paradigma o Social IoT (SIoT), onde múltiplos tipos de relacionamentos e confiabilidade podem ser estabelecidos entre dispositivos. Neste artigo, abordamos as questões de como modelar laços sociais em IoT e na proposição de modelos para, automaticamente, classificar e predizer relações em SIoT. Assim, propomos a utilização de aprendizado por representação para classificar diferentes tipos de laços sociais em SIoT. Para isso, utiliza-se como estratégias para classificação Graph Neural Networks (GNN) ou Algoritmos Tradicionais de Classificação (ATC). Como resultados, mostramos o compromisso no uso de GNN e ATC em diferentes cenários.
Palavras-chave:
IoT, SIoT, Aprendizado por representação, Machine Learning
Referências
Luigi Atzori, Antonio Iera, Giacomo Morabito, and Michele Nitti. 2012. The Social Internet of Things (SIoT) – When social networks meet the Internet of Things: Concept, architecture and network characterization. Computer Networks 56, 16 (2012), 3594–3608.
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković. 2020. Principal neighbourhood aggregation for graph nets. arXiv preprint arXiv:2004.05718 33 (2020), 13260–13271.
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In 22nd ACM SIGKDD. 855–864.
Chitrank Gupta, Yash Jain, Abir De, and Soumen Chakrabarti. 2021. Integrating Transductive and Inductive Embeddings Improves Link Prediction Accuracy. In Proceedings of the 30th ACM International Conference on Information Knowledge Management (Virtual Event, Queensland, Australia) (CIKM ’21). Association for Computing Machinery, New York, NY, USA, 3043–3047. https://doi.org/10.1145/3459637.3482125
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs, In Proceedings of the 31st International Conference on Neural Information Processing Systems. ArXiv e-prints.
Liang Hu, Gang Wu, Yongheng Xing, and Feng Wang. 2019. Things2Vec: Semantic modeling in the Internet of Things with graph representation learning. IEEE Internet of Things Journal (2019).
Abdullah Khanfor, Amal Nammouchi, Hakim Ghazzai, Ye Yang, Mohammad R Haider, and Yehia Massoud. 2020. Graph neural networks-based clustering for social internet of things. In 63rd MWSCAS. IEEE.
Claudio Marche and Luigi Atzori. 2018. A Dataset for Performance Analysis of the Social Internet of Things. In IEEE 29th PIMRC.
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. 2019. Weisfeiler and leman go neural: Higher-order graph neural networks. In AAAI.
Quentin Oliveau and Hichem Sahbi. 2018. From Transductive to Inductive Semi-Supervised Attributes for Ship Category Recognition. In IEEE IGARSS.
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković. 2020. Principal neighbourhood aggregation for graph nets. arXiv preprint arXiv:2004.05718 33 (2020), 13260–13271.
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In 22nd ACM SIGKDD. 855–864.
Chitrank Gupta, Yash Jain, Abir De, and Soumen Chakrabarti. 2021. Integrating Transductive and Inductive Embeddings Improves Link Prediction Accuracy. In Proceedings of the 30th ACM International Conference on Information Knowledge Management (Virtual Event, Queensland, Australia) (CIKM ’21). Association for Computing Machinery, New York, NY, USA, 3043–3047. https://doi.org/10.1145/3459637.3482125
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs, In Proceedings of the 31st International Conference on Neural Information Processing Systems. ArXiv e-prints.
Liang Hu, Gang Wu, Yongheng Xing, and Feng Wang. 2019. Things2Vec: Semantic modeling in the Internet of Things with graph representation learning. IEEE Internet of Things Journal (2019).
Abdullah Khanfor, Amal Nammouchi, Hakim Ghazzai, Ye Yang, Mohammad R Haider, and Yehia Massoud. 2020. Graph neural networks-based clustering for social internet of things. In 63rd MWSCAS. IEEE.
Claudio Marche and Luigi Atzori. 2018. A Dataset for Performance Analysis of the Social Internet of Things. In IEEE 29th PIMRC.
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. 2019. Weisfeiler and leman go neural: Higher-order graph neural networks. In AAAI.
Quentin Oliveau and Hichem Sahbi. 2018. From Transductive to Inductive Semi-Supervised Attributes for Ship Category Recognition. In IEEE IGARSS.
Publicado
07/11/2022
Como Citar
J. JUNIOR, Jamisson; FIGUEIREDO, Thiago S.; LOPES, Ramon; SANTOS, Bruno P.; TORRES, Luiz C. B..
Utilizando aprendizado por representação para a classificação de laços sociais da IoT. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 28. , 2022, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 65-68.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2022.226525.
