Comparar métodos

	Análise de Difusão em Redes Temporais	Centralidade de Intermediação Temporal
Área	Análise de redes	Análise de redes
Família	Machine learning	Machine learning
Ano de origem	2012	2012
Autor original	Holme, P. & Saramäki, J.	Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.
Tipo	Network analysis framework	Centrality measure for temporal networks
Fonte seminal	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Outros nomes	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness
Relacionados	5	6
Resumo	Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss.	Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot.