TgraphSpot: fast and effective anomaly detection for time-evolving graphs (2022)
Cazzolato, Mirela Teixeira 
; Vijayakumar, Saranya; Zheng, Xinyi; Park, Namyong; Lee, Meng-Chieh; Fidalgo, Pedro; Lages, Bruno; Traina, Agma Juci Machado ; Faloutsos, Christos
Source: Proceedings. Conference titles: IEEE International Conference on Big Data - Big Data. Unidade: ICMC
Subjects: RECUPERAÇÃO DA INFORMAÇÃO, VISUALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAZZOLATO, Mirela Teixeira et al. TgraphSpot: fast and effective anomaly detection for time-evolving graphs. 2022, Anais.. Piscataway: IEEE, 2022. Disponível em: https://doi.org/10.1109/BigData55660.2022.10020898. Acesso em: 08 dez. 2025.APA
Cazzolato, M. T., Vijayakumar, S., Zheng, X., Park, N., Lee, M. -C., Fidalgo, P., et al. (2022). TgraphSpot: fast and effective anomaly detection for time-evolving graphs. In Proceedings. Piscataway: IEEE. doi:10.1109/BigData55660.2022.10020898NLM
Cazzolato MT, Vijayakumar S, Zheng X, Park N, Lee M-C, Fidalgo P, Lages B, Traina AJM, Faloutsos C. TgraphSpot: fast and effective anomaly detection for time-evolving graphs [Internet]. Proceedings. 2022 ;[citado 2025 dez. 08 ] Available from: https://doi.org/10.1109/BigData55660.2022.10020898Vancouver
Cazzolato MT, Vijayakumar S, Zheng X, Park N, Lee M-C, Fidalgo P, Lages B, Traina AJM, Faloutsos C. TgraphSpot: fast and effective anomaly detection for time-evolving graphs [Internet]. Proceedings. 2022 ;[citado 2025 dez. 08 ] Available from: https://doi.org/10.1109/BigData55660.2022.10020898
