방법 비교

	시간적 네트워크 확산 분석	시간적 사회 연결망 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2012	2000s–2010s
창시자	Holme, P. & Saramäki, J.	Moody, J.; Holme, P.; Saramäki, J.
유형	Network analysis framework	Longitudinal network analysis
원전	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.
별칭	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
관련	5	4
요약	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 Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time.