Variational Estimators for Node Popularity Models
This work addresses the need for accurate and scalable node popularity estimation in network modeling, particularly for bipartite networks, but it is incremental as it builds on existing models like TNPM.
The paper tackles the problem of estimating node popularity in bipartite and undirected networks by developing a variational expectation-maximization framework for the Two-Way Node Popularity Model, achieving superior estimation accuracy compared to existing algorithms in simulations and real-world evaluations.
Node popularity is recognized as a key factor in modeling real-world networks, capturing heterogeneity in connectivity across communities. This concept is equally important in bipartite networks, where nodes in different partitions may exhibit varying popularity patterns, motivating models such as the Two-Way Node Popularity Model (TNPM). Existing methods, such as the Two-Stage Divided Cosine (TSDC) algorithm, provide a scalable estimation approach but may have limitations in terms of accuracy or applicability across different types of networks. In this paper, we develop a computationally efficient and theoretically justified variational expectation-maximization (VEM) framework for the TNPM. We establish label consistency for the estimated community assignments produced by the proposed variational estimator in bipartite networks. Through extensive simulation studies, we show that our method achieves superior estimation accuracy across a range of bipartite as well as undirected networks compared to existing algorithms. Finally, we evaluate our method on real-world bipartite and undirected networks, further demonstrating its practical effectiveness and robustness.