Yöntem Karşılaştırma

	Cosponsorship Network Analysis	Topluluk Tespiti	Manifesto Coding
Alan	Political Science	Ağ analizi	Political Science
Aile	Process / pipeline	Process / pipeline	Process / pipeline
Köken yılı	2006	2002–2019 (algorithm family)	2001
Köken	James H. Fowler	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR)
Tür	Social-network analysis of legislative collaboration	Graph-partitioning / clustering algorithm family	Quantitative content analysis of party manifestos
Seminal kaynak	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.	Budge, I., Klingemann, H.-D., Volkens, A., Bara, J., & Tanenbaum, E. (2001). Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945–1998. Oxford: Oxford University Press. ISBN: 9780199244003
Diğer adlar	Cosponsorship networks, Legislative collaboration networks, Bill cosponsorship analysis, Co-sponsorship network analysis	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	CMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding
İlişkili	3	5	4
Özet	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?	Manifesto coding is the quantitative content-analysis methodology of the Comparative Manifesto Project (CMP/MARPOR) for measuring parties' policy preferences from their election manifestos. Trained coders break each manifesto into quasi-sentences and assign every unit to one of a fixed set of policy categories. Counting how often each category appears yields salience measures, and combining pro- and anti- categories produces position scores such as the left–right RILE index, giving comparable estimates of party positions across more than fifty democracies since 1945.