Open Access10.5075/EPFL-THESIS-5807
Relational Learning with Hypergraphs
Li Pu
- 01 Jan 2013
6
TL;DR: This paper presents a meta-analyses of relational learning, spectral graph theory, and recommender system for network traffic inspection at the Ecole polytechnique federale de Lausanne EPFL in 2013.
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Abstract: Keywords: relational learning ; hypergraph learning ; spectral graph theory ; recommender system ; network traffic inspection These Ecole polytechnique federale de Lausanne EPFL, n° 5807 (2013)Programme doctoral Informatique, Communications et InformationFaculte informatique et communicationsInstitut d'informatique fondamentaleLaboratoire d'intelligence artificielleJury: M. Seeger (president), K. Stoffel, P. Vandergheynst, Q. Yang Public defense: 2013-7-12 Reference doi:10.5075/epfl-thesis-5807Print copy in library catalog Record created on 2013-07-01, modified on 2017-05-12
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Citations
•Posted Content
Adjacency and Tensor Representation in General Hypergraphs Part 1: e-adjacency Tensor Uniformisation Using Homogeneous Polynomials
TL;DR: This paper contributes in a uniformization process of a general hypergraph to allow the definition of an e-adjacency tensor, viewed as a hypermatrix, reflecting the general hyper graph structure.
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•Posted Content
Sparse Polynomial Learning and Graph Sketching
TL;DR: In this paper, the authors give an algorithm for hypergraph sketching from uniformly drawn random cuts that runs in time polynomial in $n$ and $2s$ and succeeds if the function satisfies the unique sign property: there is one output value which corresponds to a unique set of values of the participating parities.
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•Posted Content
Adjacency Matrix and Co-occurrence Tensor of General Hypergraphs: Two Well Separated Notions
Xavier Ouvrard,Stéphane Marchand-Maillet +1 more
- 21 Dec 2017
TL;DR: A novel way of building a symmetric co-occurrence hypermatrix is proposed that captures also the cardinality of the hyperedges and allows full separation of the different layers of the hypergraph.
2
Tensorized Hypergraph Neural Networks
TL;DR: The Tensorized Hypergraph Neural Network (THNN) as discussed by the authors is a tensor extension of the adjacency-matrix-based graph neural networks, which is equivalent to an high-order polynomial regression scheme.
•Proceedings Article
Sparse Polynomial Learning and Graph Sketching
Murat Kocaoglu,Karthikeyan Shanmugam,Alexandros G. Dimakis,Adam R. Klivans +3 more
- 08 Dec 2014
TL;DR: An algorithm for exactly reconstructing f given random examples from the uniform distribution on {- 1,1}n that runs in time polynomial in n and 2s and succeeds if the function satisfies the unique sign property: there is one output value which corresponds to a unique set of values of the participating parities.
References
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Computers and Intractability: A Guide to the Theory of NP-Completeness
Michael Randolph Garey,David S. Johnson +1 more
- 01 Jan 1979
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
The Structure and Function of Complex Networks
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
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