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  2. Laplacian matrix - Wikipedia

    en.wikipedia.org/wiki/Laplacian_matrix

    A vertex with a large degree, also called a heavy node, results in a large diagonal entry in the Laplacian matrix dominating the matrix properties. Normalization is aimed to make the influence of such vertices more equal to that of other vertices, by dividing the entries of the Laplacian matrix by the vertex degrees.

  3. Calculus on finite weighted graphs - Wikipedia

    en.wikipedia.org/wiki/Calculus_on_finite...

    In applications vertex functions are useful to label the vertices of the nodes. For example, in graph-based data clustering, each node represents a data point and a vertex function is used to identify cluster membership of the nodes.

  4. Spectral clustering - Wikipedia

    en.wikipedia.org/wiki/Spectral_clustering

    The general approach to spectral clustering is to use a standard clustering method (there are many such methods, k-means is discussed below) on relevant eigenvectors of a Laplacian matrix of . There are many different ways to define a Laplacian which have different mathematical interpretations, and so the clustering will also have different ...

  5. Discrete Laplace operator - Wikipedia

    en.wikipedia.org/wiki/Discrete_Laplace_operator

    In mathematics, the discrete Laplace operator is an analog of the continuous Laplace operator, defined so that it has meaning on a graph or a discrete grid.For the case of a finite-dimensional graph (having a finite number of edges and vertices), the discrete Laplace operator is more commonly called the Laplacian matrix.

  6. Spectral graph theory - Wikipedia

    en.wikipedia.org/wiki/Spectral_graph_theory

    The smallest pair of cospectral mates is {K 1,4, C 4 ∪ K 1}, comprising the 5-vertex star and the graph union of the 4-vertex cycle and the single-vertex graph [1]. The first example of cospectral graphs was reported by Collatz and Sinogowitz [2] in 1957. The smallest pair of polyhedral cospectral mates are enneahedra with eight vertices each ...

  7. Expander graph - Wikipedia

    en.wikipedia.org/wiki/Expander_graph

    Here one considers the matrix ⁠ 1 / d ⁠ A, which is the Markov transition matrix of the graph G. Its eigenvalues are between −1 and 1. For not necessarily regular graphs, the spectrum of a graph can be defined similarly using the eigenvalues of the Laplacian matrix.

  8. Graph theory - Wikipedia

    en.wikipedia.org/wiki/Graph_theory

    The Laplacian matrix is a modified form of the adjacency matrix that incorporates information about the degrees of the vertices, and is useful in some calculations such as Kirchhoff's theorem on the number of spanning trees of a graph.

  9. Degree matrix - Wikipedia

    en.wikipedia.org/wiki/Degree_matrix

    In the mathematical field of algebraic graph theory, the degree matrix of an undirected graph is a diagonal matrix which contains information about the degree of each vertex—that is, the number of edges attached to each vertex. [1]