เปรียบเทียบวิธี

	Cosponsorship Network Analysis	การตรวจจับชุมชน
สาขาวิชา	Political Science	การวิเคราะห์เครือข่าย
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2006	2002–2019 (algorithm family)
ผู้ริเริ่ม	James H. Fowler	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
ประเภท	Social-network analysis of legislative collaboration	Graph-partitioning / clustering algorithm family
แหล่งต้นตำรับ	Fowler, J. H. (2006). Connecting the Congress: A Study of Cosponsorship Networks. Political Analysis, 14(4), 456–487.	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.
ชื่อเรียกอื่น	Cosponsorship networks, Legislative collaboration networks, Bill cosponsorship analysis, Co-sponsorship network analysis	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
ที่เกี่ยวข้อง	3	5
สรุป	Cosponsorship network analysis treats legislative collaboration as a social network: when legislators cosponsor one another's bills, they form ties, and the resulting web of connections can be measured with the tools of network science. Introduced to congressional studies by James Fowler in 2006, it turns the public record of who signed on to whose bills into a graph among lawmakers, revealing who is central and influential, how connected the chamber is, and which clusters of legislators form coalitions. With inferential network models such as ERGMs, researchers move from describing the network to explaining why ties form.	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?