Inference

Principled inference of hyperedges and overlapping communities in hypergraphs

Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on statistical inference to …

Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships

The use of patent citation networks as research tools is becoming increasingly commonplace in the field of innovation studies. However, these networks rarely consider the contexts in which these citations are generated and are generally restricted to …

Estimating Social Influence from Observational Data

We consider the problem of estimating social influence, the effect that a person’s behavior has on the future behavior of their peers. The key challenge is that shared behavior between friends could be equally explained by influence or by …

Reciprocity, community detection, and link prediction in dynamic networks

Many complex systems change their structure over time, in these cases dynamic networks can provide a richer representation of such phenomena. As a consequence, many inference methods have been generalized to the dynamic case with the aim to model …

Community detection and reciprocity in networks by jointly modeling pairs of edges

We present a probabilistic generative model and an efficient algorithm to both perform community detection and capture reciprocity in networks. Our approach jointly models pairs of edges with exact 2-edge joint distributions. In addition, it provides …

Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data

Social network data are often constructed by incorporating reports from multiple individuals. However, it is not obvious how to reconcile discordant responses from individuals. There may be particular risks with multiply-reported data if people's …

The interplay between ranking and communities in networks

Community detection and hierarchy extraction are usually thought of as separate inference tasks on networks. Considering only one of the two when studying real-world data can be an oversimplification. In this work, we present a generative model based …

A generative model for reciprocity and community detection in networks

We present a probabilistic generative model and efficient algorithm to model reciprocity in directed networks. Unlike other methods that address this problem such as exponential random graphs, it assigns latent variables as community memberships to …

Inference on networks

Complex interacting systems are often represented by large datasets containing a considerable amount of information. The question is how to capture the relevant macroscopic behavior by retaining only a small amount of information.

Community detection with node attributes in multilayer networks

Community detection in networks is commonly performed using information about interactions between nodes. Recent advances have been made to incorporate multiple types of interactions, thus generalising standard methods to multilayer networks. Often …