Learning with Hypergraphs: Clustering, Classification, and Embedding

By Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf. 2006

1,589 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics

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Citations in the corpus

Learning on Weighted Hypergraphs to Integrate Protein Interactions and Gene Expressions for Cancer Outcome Prediction (2008, positive): “We compare the classification performance of HyperGene with three baselines, the hypergraph-based learning algorithm [22] and SVMs with linear kernel and RBF kernel (Matlab Bioinformatics Toolbox (V3.”
An Attribute-Assisted Reranking Model for Web Image Search (2015, methodology): “For hypergraph learning, the Normalized Laplacian method proposed in [15] is employed, and it is formulated as a regularization framework:”
Ieee Transactions on Knowledge and Data Engineering Context-aware Hypergraph Construction for Robust Spectral Clustering (neutral): “More recently, hypergraph analysis [17], [18] has emerged as a popular tool for addressing issues iii) and iv).”
Ieee Transactions on Pattern Analysis and Machine Intelligence Laplacian Regularized Low-rank Representation and Its Applications (neutral): “Then the unnormalized hyper-Laplacian matrix [52] can be defined as, L h = D V − HW E D −1 E H T (6) where W E is the diagonal matrix of the edge weight W (e).”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, background): “Zhou et al. (2006) have proposed normalized hypergraph cut that is a generalized version of the spectral clustering method.”
Click Prediction for Web Image Reranking Using Multimodal Sparse Coding (2014, methodology): “we implement hypergraph learning [29] to construct a group of manifolds, which preserves local smoothness using hyperedges.”
HGNN+: General Hypergraph Neural Networks (2022, methodology): “We first define the random walk in a hypergraph following [20], [34], [35], [36].”
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs (2018, methodology): “We conducted experiments not only on real-world datasets but also on categorical data (results in supplementary) which are a standard practice in hypergraph-based learning [52, 22, 49, 30, 29, 27].”
Music recommendation by unified hypergraph: combining social media information and music content (2010, methodology): “Our work is also related to graph-based ranking and hypergraph learning [45, 2, 3, 44, 12, 35, 7].”
Multi-view hypergraph learning by patch alignment framework (2013, methodology): “In the part optimization stage, different approaches have different optimization criteria over patches, each of which is built by one measurement associated with its related ones.”
Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications (2013, background): “Denote A, E as the vertex set and the hyperedge set corresponding to a hypergraph of GðA;EÞ [37].”
Contextual Hypergraph Modeling for Salient Object Detection (2013, background): “E = {ej} is the hyperedge set comprising a family of subsets of V such that ⋃ e∈E = V [26].”
Image retrieval via probabilistic hypergraph ranking (2010, methodology): “In [17] a hypergraph cut algorithm [34] is adopted to solve the unsupervised video ob- Figure 1.”
3-D Object Retrieval and Recognition With Hypergraph Analysis (2012, methodology): “It is worth emphasizing that graph sparsification methods [37] can be adopted to reduce the computational costs.”
Networks beyond pairwise interactions: structure and dynamics (2020, methodology): “[396], and finally to assess their stability together with Lohse in Ref.”