Bandingkan kaedah

	Analisis Rangkaian Temporal Berbobot	Pengesanan Komuniti Berpemberat
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2004–2012	2004–2008
Pengasas	Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)	Newman, M. E. J.; Blondel et al.
Jenis	Network analysis technique	Graph clustering / community detection
Sumber perintis	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.
Alias	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
Berkaitan	6	6
Ringkasan	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.