12 citations · 13 across the 5 of their papers we have counts for
6 papers
Estimating Network Models using Neural Networks
Angelo Mele
Exponential random graph models (ERGMs) are very flexible for modeling network formation but pose difficult estimation challenges due to their intractable normalizing constant. Exi…
A model of inter-organizational network formation
Shweta Gaonkar, Angelo Mele
How do inter-organizational networks emerge? Accounting for interdependence among ties while studying tie formation is one of the key challenges in this area of research. We addres…
A Structural Model of Business Card Exchange Networks
Juan Nelson Martínez Dahbura, Shota Komatsu, Takanori Nishida +1
Social and professional networks affect labor market dynamics, knowledge diffusion and new business creation. To understand the determinants of how these networks are formed in the…
On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates
Cong Mu, Angelo Mele, Lingxin Hao +3
In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity.…
Spectral inference for large Stochastic Blockmodels with nodal covariates
Angelo Mele, Lingxin Hao, Joshua Cape +1
In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. To this end, we develop spectral estim…
Approximate Variational Estimation for a Model of Network Formation
Angelo Mele, Lingjiong Zhu
We develop approximate estimation methods for exponential random graph models (ERGMs), whose likelihood is proportional to an intractable normalizing constant. The usual approach a…