Search Results for author:
Found 16 papers, 13 papers with code
Hypergraphs with node attributes: structure and inference
Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individual in a workplace.
A model for efficient dynamical ranking in networks
We present a physics-inspired method for inferring dynamic rankings in directed temporal networks - networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction.
Immiscible Color Flows in Optimal Transport Networks for Image Classification
In classification tasks, it is crucial to meaningfully exploit the information contained in data.
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.
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 interplay between ranking and communities in networks
It assumes that each node has a preference in the interaction mechanism and nodes with the same preference are more likely to interact, while heterogeneous interactions are still allowed.
Optimal transport in multilayer networks for traffic flow optimization
Modeling traffic distribution and extracting optimal flows in multilayer networks is of utmost importance to design efficient multi-modal network infrastructures.
Principled network extraction from images
Images of natural systems may represent patterns of network-like structure, which could reveal important information about the topological properties of the underlying subject.
Generative model for reciprocity and community detection in networks
Inference is performed using an efficient expectation-maximization algorithm that exploits the sparsity of the network, leading to an efficient and scalable implementation.
Community Detection
Social and Information Networks
Physics and Society
Community detection with node attributes in multilayer networks
Here we develop a method that incorporates both the topology of interactions and node attributes to extract communities in multilayer networks.
Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures
On the other hand, to demonstrate more broadly how sampling can impact the estimation of relevant network properties like centrality measures different than the one aimed at optimizing, community structure and node attribute distribution.
Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
Sampling on networks: estimating eigenvector centrality on incomplete graphs
We develop a new sampling method to estimate eigenvector centrality on incomplete networks.
Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
A physical model for efficient ranking in networks
We present a physically-inspired model and an efficient algorithm to infer hierarchical rankings of nodes in directed networks.
Community detection, link prediction, and layer interdependence in multilayer networks
In particular, this allows us to bundle layers together to compress redundant information, and identify small groups of layers which suffice to predict the remaining layers accurately.
Social and Information Networks Statistical Mechanics Physics and Society
Phase transitions and optimal algorithms in high-dimensional Gaussian mixture clustering
We consider the problem of Gaussian mixture clustering in the high-dimensional limit where the data consists of $m$ points in $n$ dimensions, $n, m \rightarrow \infty$ and $\alpha = m/n$ stays finite.
Dynamics of beneficial epidemics
Third, in the context of dynamic social networks, we find that preferences for increased global infection accelerate spread and produce superexponential fixation, but preferences for local assortativity halt epidemics by disconnecting the infected from the susceptible.
Physics and Society Multiagent Systems Social and Information Networks Adaptation and Self-Organizing Systems Populations and Evolution
