Likelihoods for fixed rank nomination networks

Link and subgraph likelihoods in random undirected networks with fixed and partially fixed degree sequences.

The simplest null models for networks, used to distinguish significant features of a particular network from a priori expected features, are random ensembles with the degree sequence fixed by the specific network of interest. These "fixed degree sequence" (FDS) ensembles are, however, famously resistant to analytic attack. In this paper we introduce ensembles with partially-fixed degree sequenc...

Dyadic data analysis with amen

Dyadic data on pairs of objects, such as relational or social network data, often exhibit strong statistical dependencies. Certain types of second-order dependencies, such as degree heterogeneity and reciprocity, can be well-represented with additive random effects models. Higher-order dependencies, such as transitivity and stochastic equivalence, can often be represented with multiplicative ef...

A Holistic Approach for Link Prediction in Multiplex Networks

Networks extracted from social media platforms frequently include multiple types of links that dynamically change over time; these links can be used to represent dyadic interactions such as economic transactions, communications, and shared activities. Organizing this data into a dynamic multiplex network, where each layer is composed of a single edge type linking the same underlying vertices, c...

Front End rank - likelihoods

Efficient vaccination strategies for epidemic control using network information

Background: Social and contact networks affect both epidemic spread and intervention implementation. Network-based interventions are most powerful when the full network structure is known. However, in practice, resource constraints require decisions to be made based on partial network information. We investigated how the effectiveness of network-based vaccination schemes varied based on the acc...