A0362
Title: Likelihood of weight loss or ACRONYM: Augmented degree corrected community reticulately organized network yielding model
Authors: Benjamin Leinwand - Stevens Institute of Technology (United States) [presenting]
Vince Lyzinski - University of Maryland, College Park (United States)
Abstract: Modeling networks can serve as a means of summarizing high-dimensional complex systems. Adapting an approach devised for dense, weighted networks, a new method is proposed for generating and estimating unweighted networks. This approach can describe a broader class of potential networks than existing models, including those where nodes in different "modules" connect to one another via various attachment mechanisms, inducing flexible and varied community structures. While unweighted edges provide less resolution than continuous weights, restricting to the binary case permits the use of likelihood-based estimation techniques, which can improve the estimation of nodal features. The extra flexibility may contribute to a different understanding of network-generating structures, particularly for networks with heterogeneous densities in different regions.
