Generalized blockmodeling

By Patrick Doreian, V. Batagelj, A. Ferligoj. Structural analysis in the social sciences, 2005

345 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b8f8ec72aa223ab51e303e3791d20be074221754

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In Strategy and Management

Citations in the corpus

Node centrality in weighted networks: Generalizing degree and shortest paths (2010, background): “One of these avenues of research has focused on the issue of tie strength, and a number of studies from a wide range of fields have begun to explore this issue (Barrat et al., 2004; Brandes, 2001; Doreian et al., 2005; Freeman et al., 1991; Granovetter, 1973; Newman, 2001; Opsahl and Panzarasa, 2009”
Social network analysis: A methodological introduction (2008, background): “Some more recent innovations can be found in Carrington, Scott, and Wasserman (2005) and Doreian, Batagelj , and Ferlioj (2005), while Scott (1991) and Degenne and Forsé (1999) serve as accessible introductions to the field.”
Model‐based clustering for social networks (2007, methodology): “Generalized blockmodelling (Doreian et al., 2005) permits ideal block types defining network equivalence to differ across pairs of blocks.”
Link Prediction in Complex Networks: A Survey (2010, background): “Stochastic block model [84,85,86,87] is one of the most general network models, where nodes are partitioned into groups and the probability that two nodes are connected depends solely on the groups to which they belong.”
Mixed Membership Stochastic Blockmodels (2007, neutral): “In statistics, this line of research has been extended in various contexts over the years (Fienberg et al., 1985; Wasserman and Pattison, 1996; Snijders, 2002; Hoff et al., 2002; Doreian et al., 2004).”
Link Prediction via Matrix Factorization (2011, background): “[4] assumes that nodes interact solely through their class memberships.”
Community detection in graphs (2009, methodology): “…inference (Winkler, 2003), in which the best fit is obtained through the maximization of a likelihood (generative models), but we also discuss related techniques, based on blockmodeling (Doreian et al., 2005), model selection (Burnham and Anderson, 2002) and information theory (Mackay, 2003).”
Core-Periphery Structure in Networks (2012, background): “, 15, 16, 19, 21, and 23) in the Zachary Karate Club network are automorphs of each other (such nodes are role equivalent [18, 20]), as one can swap their labels without changing the network structure.”
network: A Package for Managing Relational Data in R (2008, neutral): “See also Doreian, et al. (2005).)”
Multilayer networks (2013, methodology): “The earliest attempts to find structures similar to communities — as well as more general types of mesoscale features — in a multilayer network comes from the social-networks literature, where blockmodels were used to find sets of nodes that all share similar connection patterns [33, 112, 152, 350].”
Opinion Dynamics and the Evolution of Social Power in Influence Networks (2015, background): “Classic problems of interest include comparative static analyses of social network structures [75, 24], functional implications of network structures [60], and numerical taxonomies of nodes [69, 10].”
Interbank Tiering and Money Center Banks (2010, neutral): “Doing so for the German system would help distinguish regional intermediaries from the (few) genuine core banks intermediating across the entire country.21 20A row-functional block (Doreian et al. (2005)) in our context implies that every bank in the periphery relates to a single bank in the core.”
MatrixExplorer: a Dual-Representation System to Explore Social Networks (2006, methodology): “Blockmodeling [13] intends to achieve this objective by decomposing the matrix in blocks of different shapes, using either clustering methods or equivalence relations defined on the graph.”
A Mathematical Bibliography of Signed and Gain Graphs and Allied Areas (2018, methodology): “A later analysis via triples is in Doreian and Krackhardt (2001a).] (SD: Bal, Clu)”