An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large networks . By utilizing  matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) models , while being able to meaningfully model local information ( degrees) as well as global information (  clustering coefficient, assortativity, etc.) if desired. This allows one to efficiently generate random networks with similar properties as an observed network, scalable up to sparse graphs consisting of millions of nodes. Empirical evaluation demonstrates its competitiveness in terms of  accuracy with state-of-the-art methods  for link prediction and network reconstruction .
An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs . By utilizing fast matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions , while being able to meaningfully model both local information of the graph (e.g., degrees) as well as global information ( e.g., clustering coefficient, assortativity, etc.) if desired. This allows one to efficiently generate random networks with similar properties as an observed network, and the models can be used for several downstream tasks such as link prediction. Our methods are scalable to sparse graphs consisting of millions of nodes. Empirical evaluation demonstrates competitiveness in terms of both speed and accuracy with state-of-the-art methods -- which are typically based on embedding the graph into some low-dimensional space -- for link prediction , showcasing the potential of a more direct and interpretable probabalistic model for this task .
R
networks
graphs
A
fast
R
models
distributions
R
local information (
both local information of the graph (e.g.,
A
e.g.,
R
scalable up
and the models can be used for several downstream tasks such as link prediction. Our methods are scalable
D
its
A
both speed and
A
-- which are typically based on embedding the graph into some low-dimensional space --
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and network reconstruction
, showcasing the potential of a more direct and interpretable probabalistic model for this task