TgraphSpot: fast and effective anomaly detection for time-evolving graphs (2022)
- Authors:
- Cazzolato, Mirela Teixeira
- Vijayakumar, Saranya - Carnegie Mellon University (CMU)
- Zheng, Xinyi - Carnegie Mellon University (CMU)
- Park, Namyong - Carnegie Mellon University (CMU)
- Lee, Meng-Chieh - Carnegie Mellon University (CMU)
- Fidalgo, Pedro
- Lages, Bruno
- Traina, Agma Juci Machado
- Faloutsos, Christos - Carnegie Mellon University (CMU)
- Cazzolato, Mirela Teixeira
- USP affiliated authors: TRAINA, AGMA JUCI MACHADO - ICMC ; CAZZOLATO, MIRELA TEIXEIRA - ICMC
- Unidade: ICMC
- DOI: 10.1109/BigData55660.2022.10020898
- Subjects: RECUPERAÇÃO DA INFORMAÇÃO; VISUALIZAÇÃO
- Keywords: time-evolving graphs; graph mining; graph visualization
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2022
- Source:
- Título do periódico: Proceedings
- Conference titles: IEEE International Conference on Big Data - Big Data
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
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: 19 abr. 2024. -
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/BigData55660.2022.10020898 -
NLM
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 2024 abr. 19 ] Available from: https://doi.org/10.1109/BigData55660.2022.10020898 -
Vancouver
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 2024 abr. 19 ] Available from: https://doi.org/10.1109/BigData55660.2022.10020898 - TGRAPP: anomaly detection and visualization of large-scale call graphs
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Informações sobre o DOI: 10.1109/BigData55660.2022.10020898 (Fonte: oaDOI API)
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