There have been rapid developments in model-based clustering of graphs, also known as
block modelling, over the last ten years or so. We review different approaches and …

Bayesian (machine) learning has been playing a significant role in machine learning for a
long time due to its particular ability to embrace uncertainty, encode prior knowledge, and …

Relational model learning is useful for numerous practical applications. Many algorithms
have been proposed in recent years to tackle this important yet challenging problem …

Evolutionary clustering is a classic and helpful framework for modeling dynamic data and
has been devoted to analyzing the temporal networks recently. However, all methods based …

Stochastic block models (SBMs) have been playing an important role in modeling clusters or
community structures of network data. But, it is incapable of handling several complex …

The decision to engage in military conflict is shaped by many factors, including state-and
dyad-level characteristics as well as the state's membership in geopolitical coalitions …

Complex networks enable to represent and characterize the interactions between entities in
various complex systems which widely exist in the real world and usually generate vast …

The mechanisms of network generation have undergone extensive analysis and found
broad applications in various real‐world scenarios. Among the fruitful literature on network …

In this paper, we investigate the use of probabilistic graphical models, specifically stochastic
blockmodels, for the purpose of hierarchical entity clustering on knowledge graphs. These …

A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in
Dynamic Networks Page 1 Bayesian Analysis (2023) 18, Number 1, pp. 49–77 A Bayesian …