图的拉普拉斯矩阵学习-Laplacian Matrices of Graphs
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We all learn one way of solving linear equations when we first encounter linear
algebra: Gaussian Elimination. In this survey, I will tell the story of some remarkable
connections between algorithms, spectral graph theory, functional analysis
and numerical linear algebra that arise in the search for asymptotically faster algorithms.
I will only consider the problem of solving systems of linear equations
in the Laplacian matrices of graphs. This is a very special case, but it is also a
very interesting case. I begin by introducing the main characters in the story.
1. Laplacian Matrices and Graphs. We will consider weighted, undirected,
simple graphs G given by a triple (V,E,w), where V is a set of vertices, E
is a set of edges, and w is a weight function that assigns a positive weight to
every edge. The Laplacian matrix L of a graph is most naturally defined by
the quadratic form it induces. For a vector x ∈ IRV , the Laplacian quadratic
form of G is:
Thus, L provides a measure of the smoothness of x over the edges in G. The
more x jumps over an edge, the larger the quadratic form becomes.
The Laplacian L also has a simple description as a matrix. Define the
weighted degree of a vertex u by:
Define D to be the diagonal matrix whose diagonal contains d, and define
the weighted adjacency matrix of G by:
We have
L = D − A.
It is often convenient to consider the normalized Laplacian of a graph instead
of the Laplacian. It is given by D−1/2LD−1/2, and is more closely related to
the behavior of random walks.
Regression on Graphs. Imagine that you have been told the value of a
function f on a subset W of the vertices of G, and wish to estimate the
values of f at the remaining vertices. Of course, this is not possible unless
f respects the graph structure in some way. One reasonable assumption is
that the quadratic form in the Laplacian is small, in which case one may
estimate f by solving for the function f : V → IR minimizing f TLf subject
to f taking the given values on W (see [ZGL03]). Alternatively, one could
assume that the value of f at every vertex v is the weighted average of f at
the neighbors of v, with the weights being proportional to the edge weights.
In this case, one should minimize:
|D-1Lf|
subject to f taking the given values on W. These problems inspire many
uses of graph Laplacians in Machine Learning.
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