Сравнение методов

	Взвешенный анализ временных сетей	Взвешенное обнаружение сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2004–2012	2004–2008
Автор метода	Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)	Newman, M. E. J.; Blondel et al.
Тип	Network analysis technique	Graph clustering / community detection
Основополагающий источник	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
Другие названия	WTNA, weighted time-varying network analysis, weighted dynamic network analysis, weighted evolving network analysis	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Связанные	6	6
Сводка	Weighted temporal network analysis studies networks whose edges carry numerical weights — representing interaction strength, frequency, or intensity — and whose structure changes over time. It combines the time-varying perspective of temporal network analysis with the quantitative precision of weighted graph metrics, revealing not only when connections exist but how strong they are at each moment.	Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation.