Random graph models are important constructs for data analytic applications as well as
pure mathematical developments, as they provide capabilities for network synthesis and …

Social network data are complex and dependent data. At the macro-level, social networks
often exhibit clustering in the sense that social networks consist of communities; and at the …

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 …

Statistical analysis of social networks provides valuable insights into complex network
interactions across various scientific disciplines. However, accurate modeling of networks …

Temporal exponential random graph models (TERGM) are powerful statistical models that
can be used to infer the temporal pattern of edge formation and elimination in complex …

Data-driven analysis of large social networks has attracted a great deal of research interest.
In this paper, we investigate 120 real social networks and their measurement-calibrated …

With the growth of interest in network data across fields, the Exponential Random Graph
Model (ERGM) has emerged as the leading approach to the statistical analysis of network …

Due to the recent availability of large complex networks, considerable analysis has focused
on understanding and characterizing the properties of these networks. Scalable generative …

Exponential random graph models, or ERGMs, are a flexible and general class of models for
modeling dependent data. While the early literature has shown them to be powerful in …

Graph datasets are frequently constructed by a projection of a bipartite graph, where two
nodes are connected in the projection if they share a common neighbor in the bipartite …