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Finite symmetric networks and loop removal
Definition
A network consists of a nonempty finite vertex set and a real matrix that is symmetric and entrywise nonnegative. A diagonal entry is a loop weight. Its loop-free part is , where the diagonal matrix has entries and all off-diagonal entries zero. The support graph has vertex set and edge set . Define the degree , counting the diagonal entry once, and put . The network Laplacian is .
The matrix is also the weighted Laplacian of : . A simple unweighted graph is represented by the special case and for all ; its edges are precisely the pairs with .
Facts & Assumptions
A finite simple graph has edges that are two-element subsets of its vertex set; its adjacency matrix has entry one exactly at adjacent distinct vertices. See A finite simple graph is a finite vertex set together with a set of two-element vertex subsets and The adjacency matrix of a finite simple graph.
For a finite symmetric nonnegative matrix with zero diagonal, the weighted Laplacian is the diagonal matrix of row sums minus that matrix, and its support graph has an edge exactly at a positive entry. See Finite weighted graph Laplacian.
A finite square matrix over is a function on a finite square index set; a listing of identifies a -indexed real array with such a matrix. See Finite rectangular matrices over a commutative ring, their entries, rows and columns.
A sum of real numbers indexed by a finite set is the sum in the additive commutative monoid of , computed using any enumeration; its value is independent of that enumeration. See A finite sum in a commutative monoid indexed by an arbitrary finite set.
Justification
Given: A network as defined above.
Goal: Verify the support-graph and Laplacian constructions and the simple-graph special case.
1.1 Choose a listing , where . The array becomes the square real matrix with entries ; diagonal matrices and matrix differences here are formed entrywise. The row sums in this listing agree with the sums indexed by and do not depend on the listing. The matrix is symmetric and nonnegative, has zero diagonal, and has for . Thus its positive off-diagonal entries define an unordered set of two-element subsets of , and the support graph is a finite simple graph. After any listing of , satisfies the hypotheses of the weighted-Laplacian definition. [given, F1, F2, F3, F4]
2.1 For each , . Consequently and , so the diagonal terms cancel in . If has zero diagonal and entries in , its support graph has adjacency matrix in any chosen vertex order; conversely every finite simple graph on nonempty gives such a matrix. This also shows that the degree retains a loop weight even though the Laplacian does not. [step 1.1, F1, F2, F3, F4, algebra] ∎
Depends on
- A finite simple graph is a finite vertex set together with a set of two-element vertex subsets
- The adjacency matrix of a finite simple graph
- Finite weighted graph Laplacian
- Finite rectangular matrices over a commutative ring, their entries, rows and columns
- A finite sum in a commutative monoid indexed by an arbitrary finite set
Used by · 22
- Support connectivity and the unnormalized Laplacian kernel
- A bilinear centered-adjacency estimate without regularity
- Ordered edge counts and centered cut vectors
- Independent inputs for the critical-density bounds
- One-vertex composition of phase-difference landscapes
- Hamiltonicity of a uniform fixed-degree regular graph
- Bipartite Ramanujan existence and the centered spectrum
- Adjacency, degrees, graph Laplacian, and spectral notation
- Oriented edge count between sets
- Claimed extension to nonnegative weighted graphs
- First and second order parameter inequality — Lemma 2.1
- Synchronization landscape preserved by vertex gluing
- Simple spinning graphs and cluster-constant equilibria
- A simple nonsynchronizing family approaching eleven sixteenths
- Hamiltonian cycles as finite cyclic-order events
- Loop coupling preserves the dynamics and centered Laplacian
- Loop removal in the weighted Kuramoto calculus
- Covariance under a common positive rescaling of weights
- Weighted spectral bounds control the degree without loops
- Loop-dependent weighted degree envelope
- A complete weighted network distinguishes a loop cap from spectral control
- Regular critical PSD phases: kernel and half-circle
Dependency tree · 2 levels
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