On the Laplacian Spectrum and Walk-regular Hypergraphs

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Hypergraph spectral learning for multi-label classification (2008, methodology): “3 Hypergraph Laplacian Several other methods define the “hypergraph Laplacian” using analogies from the graph Laplacian [19, 28].”
Linear and Multilinear Algebra (2007, background): “Consequently, a node has high value of EC if it is either connected to many other nodes or connected to others that themselves have high EC [13].”
Connected Hypergraphs with Small Spectral Radius (2014, unknown): “The first approach is to generalize the Laplacian spectra based on the s-th-order random walks (Rodŕıguez [20, 20] for s = 1,”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “Further insights into the structure of the hypergraphs themselves [86] and into the processes taking place over them [87, 88] can be obtained from studying the Laplacians of these networks and their spectra, which will be introduced in Sec.”
Image retrieval via probabilistic hypergraph ranking (2010, methodology): “These methods include clique expansion [37], star expansion [37], Rodriquez’s Laplacian [23], etc.”
Higher order learning with graphs (2006, background): “Rodŕıguez (Rodŕıguez, 2003; Rodŕıguez, 2002) constructs a weighted graph G(V,E = E) from an unweighted hypergraph G(V,E).”