EGBTER: capturing degree distribution, clustering coefficients, and community structure in a single random graph model
O El-Daghar, E Lundberg… - 2018 IEEE/ACM …, 2018 - ieeexplore.ieee.org
Random graph models are important constructs for data analytic applications as well as
pure mathematical developments, as they provide capabilities for network synthesis and …
pure mathematical developments, as they provide capabilities for network synthesis and …
[PDF][PDF] EGBTER: Capturing degree distribution, clustering coefficients, and community structure in a single random graph model
O El-daghar, E Lundberg, R Bridges - researchgate.net
Random graph models are important constructs for data analytic applications as well as
pure mathematical developments, as they provide capabilities for network synthesis and …
pure mathematical developments, as they provide capabilities for network synthesis and …
EGBTER: Capturing degree distribution, clustering coefficients, and community structure in a single random graph model
O El-daghar, E Lundberg, RA Bridges - arXiv e-prints, 2018 - ui.adsabs.harvard.edu
Random graph models are important constructs for data analytic applications as well as
pure mathematical developments, as they provide capabilities for network synthesis and …
pure mathematical developments, as they provide capabilities for network synthesis and …
EGBTER: Capturing degree distribution, clustering coefficients, and community structure in a single random graph model
O El-daghar, E Lundberg, RA Bridges - arXiv preprint arXiv:1808.01267, 2018 - arxiv.org
Random graph models are important constructs for data analytic applications as well as
pure mathematical developments, as they provide capabilities for network synthesis and …
pure mathematical developments, as they provide capabilities for network synthesis and …
EGBTER: Capturing Degree Distribution, Clustering Coefficients, and Community Structure in a Single Random Graph Model
O El-Daghar, E Lundberg, R Bridges - 2018 IEEE/ACM International …, 2018 - computer.org
Random graph models are important constructs for data analytic applications as well as
pure mathematical developments, as they provide capabilities for network synthesis and …
pure mathematical developments, as they provide capabilities for network synthesis and …
EGBTER: Capturing Degree Distribution, Clustering Coefficients, and Community Structure in a Single Random Graph Model
O El-Daghar, E Lundberg, RA Bridges - 2018 - osti.gov
Random graph models are important constructs for data analytic applications as well as
pure mathematical developments, as they provide capabilities for network synthesis and …
pure mathematical developments, as they provide capabilities for network synthesis and …
EGBTER: capturing degree distribution, clustering coefficients, and community structure in a single random graph model
O El-daghar, E Lundberg, R Bridges - Proceedings of the 2018 IEEE …, 2018 - dl.acm.org
Random graph models are important constructs for data analytic applications as well as
pure mathematical developments, as they provide capabilities for network synthesis and …
pure mathematical developments, as they provide capabilities for network synthesis and …
EGBTER: Capturing Degree Distribution, Clustering Coefficients, and Community Structure in a Single Random Graph Model...
O El-daghar, E Lundberg, RA Bridges - ornl.gov
Random graph models are important constructs for data analytic applications as well as
pure mathematical developments, as they provide capabilities for network synthesis and …
pure mathematical developments, as they provide capabilities for network synthesis and …