F
François Fouss
Researcher at Université catholique de Louvain
Publications - 51
Citations - 2775
François Fouss is an academic researcher from Université catholique de Louvain. The author has contributed to research in topics: Graph (abstract data type) & Shortest path problem. The author has an hindex of 19, co-authored 51 publications receiving 2527 citations. Previous affiliations of François Fouss include University College London.
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Journal ArticleDOI
Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation
TL;DR: The model, which nicely fits into the so-called "statistical relational learning" framework, could also be used to compute document or word similarities, and could be applied to machine-learning and pattern-recognition tasks involving a relational database.
Book ChapterDOI
The principal components analysis of a graph, and its relationships to spectral clustering
TL;DR: The Principal Components Analysis (PCA) of a graph is defined as the subspace projection that preserves as much variance as possible, in terms of the ECTD, a principal components analysis of the graph based on a Markov-chain model of random walk through the graph.
Journal ArticleDOI
An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification
TL;DR: In this paper, the authors present a survey of kernel-on-graphs (kernels on graphs) and two related similarity matrices, which they refer to as kernels on graph.
Proceedings ArticleDOI
An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task
TL;DR: Results indicate that a simple nearest-neighbours rule based on the similarity measure provided by the regularized Laplacian, the Markov diffusion and the commute time kernels performs best and is recommended for computing similarities between elements of a database.
Journal ArticleDOI
Randomized shortest-path problems: Two related models
TL;DR: This work revisits Akamatsu's model by recasting it into a sum-over-paths statistical physics formalism allowing easy derivation of all the quantities of interest in an elegant, unified way and shows that the unique optimal policy can be obtained by solving a simple linear system of equations.