M
Marco Saerens
Researcher at Université catholique de Louvain
Publications - 122
Citations - 4853
Marco Saerens is an academic researcher from Université catholique de Louvain. The author has contributed to research in topics: Graph (abstract data type) & Directed graph. The author has an hindex of 31, co-authored 120 publications receiving 4301 citations. Previous affiliations of Marco Saerens include Xerox & Université libre de Bruxelles.
Papers
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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.
Journal ArticleDOI
Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure
TL;DR: The original method, based on the EM algorithm, is shown to be superior to the standard one for a priori probability estimation and always performs better than the original one in terms of classification accuracy, when the a Priori probability conditions differ from the training set to the real-world data.
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.