arXiv research

NPE improves scalability and efficiency for ERGMs.

A new test assesses how well observed networks fit a specified ERGM model.

A new method selects features for ERGMs to improve network modeling.

Unified approach compares ERGM, GCN, and Word2Vec+MLP for collaboration network link prediction.

We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including maximum likelihood estimation algorithms. We discuss models of this type and their pro…

Neural network estimates network models efficiently.

A major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural properties observed in actual networks. This statistical approach becomes increasingly problematic as network size increases. In the context of curre…

We propose a novel model for generating graphs similar to a given example graph. Unlike standard approaches that compute features of graphs in Euclidean space, our approach obtains features on a surface of a hypersphere. We then utilize a von Mises-Fisher distribution, an exponential family distribution on the surface …

Unified framework for network model assessment using maximum entropy.

Contrastive divergence (CD) is a promising method of inference in high dimensional distributions with intractable normalizing constants, however, the theoretical foundations justifying its use are somewhat shaky. This document proposes a framework for understanding CD inference, how/when it works, and provides multiple…

Study evaluates RKHS choices for assessing graph models using KSD tests.

New method models aptamer libraries as Boltzmann-weighted graph ensembles for better affinity predictions.