Exponential Random Graph Models for Social Networks
By Julien Brailly, Scott Viallet-Thévenin. 2014
305 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/f5a3db4f469d3d0ce2fd7f98ae12798ec81e6719
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Xiangnan He
- Tat-seng Chua
- M. A. Porter
- Yamir Moreno
- Marc Barthelemy
- Hanwang Zhang
- Alex Arenas
- Mark Lubell
In Statistical and Nonlinear Physics
- M. A. Porter
- Alex Arenas
- Yamir Moreno
- Marc Barthelemy
- Peng Wang
- J. P. Gleeson
- Mikko Kivelä
- Santo Fortunato
Citations in the corpus
- Network structure and institutional complexity in an ecology of water management games (2014, methodology): “…hypotheses using exponential random graph models (ERGMs) that estimate the probability of network tie formation as a function of different structural characteristics of the network and attributes of actors and institutions (Wang et al. 2009, Cranmer and Desmarias 2011, Lusher et al. 2013).”
- Attributed Social Network Embedding (2017, background): “Social networks belong to a special class of networks, in which the formation of social ties involves not only the selforganizing network process, but also the attribute-based process [11].”
- Multilayer networks (2013, methodology): “The tools that have been developed to investigate multilayer social networks include exponential random graph models (ERGMs) [212, 262], meta-networks and meta-matrices [79, 190], and methods for identifying social roles using blockmodeling and relational algebras [53,59,112,210,261,356,358].”