An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification

DOI10.1016/j.neunet.2012.03.001zbMath1245.68155OpenAlexW2105295920WikidataQ51382820 ScholiaQ51382820MaRDI QIDQ448318

Luh Yen, François Fouss, Kevin Francoisse, Marco Saerens, Alain Pirotte

Publication date: 30 August 2012

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2012.03.001

zbMATH Keywords

graph miningcollaborative recommendationkernels on graphssemisupervised classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Efficient clustering of large uncertain graphs using neighborhood information ⋮ A bag-of-paths framework for network data analysis ⋮ Semi-supervised classification and betweenness computation on large, sparse, directed graphs ⋮ An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification ⋮ Unsupervised representation learning with minimax distance measures ⋮ Learning representations from dendrograms ⋮ An optimization model for collaborative recommendation using a covariance-based regularizer ⋮ Developments in the theory of randomized shortest paths with a comparison of graph node distances ⋮ Dissecting graph measure performance for node clustering in LFR parameter space ⋮ Semi-supervised learning with regularized Laplacian

Uses Software

• NetKit

Cites Work

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• An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification
• A class of graph-geodetic distances generalizing the shortest-path and the resistance distances
• Semi-supervised classification and betweenness computation on large, sparse, directed graphs
• Laplace eigenvalues of graphs---a survey
• Graphs, networks and algorithms
• Random walk with restart: fast solutions and applications
• Statistical analysis of network data. Methods and models
• The matrix-forest theorem and measuring relations in small social groups
• Modern multidimensional scaling: theory and applications
• The electrical resistance of a graph captures its commute and cover times
• Laplacians and the Cheeger inequality for directed graphs
• Spanning forests of a digraph and their applications
• Principal component analysis.
• The matrix of maximum out forests of a digraph and its applications
• On proximity measures for graph vertices
• On a duality between metrics and \(\Sigma\)-proximities
• On the properties of von Neumann kernels for link analysis
• Commute times for a directed graph using an asymmetric Laplacian
• Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
• 10.1162/15324430260185619
• Authoritative sources in a hyperlinked environment
• Detecting network communities: a new systematic and efficient algorithm
• Partitioning Sparse Matrices with Eigenvectors of Graphs
• Kernels for Structured Data
• Introduction to Information Retrieval
• Introduction to Semi-Supervised Learning
• Graph-Based Semi-Supervised Learning and Spectral Kernel Design
• Transformation of non positive semidefinite correlation matrices
• Markov Chains
• On the Optimality of the Median Cut Spectral Bisection Graph Partitioning Method
• Learning Theory
• Computing Communities in Large Networks Using Random Walks
• Learning Theory and Kernel Machines
• Semisupervised Learning for Computational Linguistics
• A Survey of Eigenvector Methods for Web Information Retrieval
• Nonlinear Dimensionality Reduction
• Machine Learning: ECML 2004
• Elements of Information Theory
• An r-Dimensional Quadratic Placement Algorithm

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