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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. 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.

  4. Algebraic connectivity - Wikipedia

    en.wikipedia.org/wiki/Algebraic_connectivity

    The algebraic connectivity (also known as Fiedler value or Fiedler eigenvalue after Miroslav Fiedler) of a graph G is the second-smallest eigenvalue (counting multiple eigenvalues separately) of the Laplacian matrix of G. [1] This eigenvalue is greater than 0 if and only if G is a connected graph. This is a corollary to the fact that the number ...

  5. Degree matrix - Wikipedia

    en.wikipedia.org/wiki/Degree_matrix

    where the degree ⁡ of a vertex counts the number of times an edge terminates at that vertex. In an undirected graph , this means that each loop increases the degree of a vertex by two. In a directed graph , the term degree may refer either to indegree (the number of incoming edges at each vertex) or outdegree (the number of outgoing edges at ...

  6. Laplacian smoothing - Wikipedia

    en.wikipedia.org/wiki/Laplacian_smoothing

    Laplacian smoothing is an algorithm to smooth a polygonal mesh. [ 1 ] [ 2 ] For each vertex in a mesh, a new position is chosen based on local information (such as the position of neighbours) and the vertex is moved there.

  7. Spectral graph theory - Wikipedia

    en.wikipedia.org/wiki/Spectral_graph_theory

    The famous Cheeger's inequality from Riemannian geometry has a discrete analogue involving the Laplacian matrix; this is perhaps the most important theorem in spectral graph theory and one of the most useful facts in algorithmic applications. It approximates the sparsest cut of a graph through the second eigenvalue of its Laplacian.