Search Results for author:
Found 7 papers, 5 papers with code
An embedding-based distance for temporal graphs
We define a distance between temporal graphs based on graph embeddings built using time-respecting random walks.
Efficient distributed representations beyond negative sampling
Our contribution is to show that the sotfmax normalization constants can be estimated in linear time, allowing us to design an efficient optimization strategy to learn distributed representations.
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs
This article unveils a new relation between the Nishimori temperature parametrizing a distribution P and the Bethe free energy on random Erdos-Renyi graphs with edge weights distributed according to P. Estimating the Nishimori temperature being a task of major importance in Bayesian inference problems, as a practical corollary of this new relation, a numerical method is proposed to accurately estimate the Nishimori temperature from the eigenvalues of the Bethe Hessian matrix of the weighted graph.
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian
This article considers the problem of community detection in sparse dynamical graphs in which the community structure evolves over time.
A unified framework for spectral clustering in sparse graphs
This article considers spectral community detection in the regime of sparse networks with heterogeneous degree distributions, for which we devise an algorithm to efficiently retrieve communities.
Optimal Laplacian regularization for sparse spectral community detection
Regularization of the classical Laplacian matrices was empirically shown to improve spectral clustering in sparse networks.
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
Spectral clustering is one of the most popular, yet still incompletely understood, methods for community detection on graphs.
