Inference

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 …

Modeling Node Exposure for Community Detection in Networks

In community detection, datasets often suffer a sampling bias for which nodes which would normally have a high affinity appear to have zero affinity. This happens for example when two affine users of a social network were not exposed to one another. …

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 …

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 …

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 …

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 …

Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures

We perform an extensive analysis of how sampling impacts the estimate of several relevant network measures. In particular, we focus on how a sampling strategy optimized to recover a particular spectral centrality measure impacts other topological …