Bandingkan kaedah

	Analisis Jaringan Sosial Berbobot	Analisis Modulariti
Bidang	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Machine learning	Machine learning
Tahun asal	2004–2010	2004
Pengasas	Barrat, A.; Opsahl, T. et al.	Newman, M. E. J. & Girvan, M.
Jenis	Network analysis framework	Community detection / graph partitioning
Sumber perintis	Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Alias	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Berkaitan	6	5
Ringkasan	Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.