How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Energy, kernel, and weighted mass balance for a finite network
Statement
Use the data of Finite weighted network diffusion data. Let and let ; its connected components are the positive-conductance components. For , let equal on and on . Then
Consequently is symmetric positive semidefinite, and exactly when is constant on each positive-conductance component and that constant is zero on every such component meeting . In the closed case , every differentiable solution of satisfies
Thus homogeneous closed diffusion conserves weighted mass , which need not equal the unweighted sum .
Facts & Assumptions
Given: A finite oriented simple graph, conductances , positive free-vertex masses , a Dirichlet set , and the matrices of Finite weighted network diffusion data.
Each oriented incidence column has exactly one at its tail and one at its head; the other entries are zero (An oriented incidence matrix of a finite simple graph).
and is its principal restriction to free vertices (Finite weighted network diffusion data).
Proof
Reversing an edge orientation replaces its incidence column by , but the contribution to does not change. For the zero extension , [F1] gives . Expanding proves the displayed energy identity. It is nonnegative term by term, so is symmetric positive semidefinite.
If , step 1.1 gives a sum of nonnegative terms equal to . For every its endpoint difference must therefore be zero. Along every path in , has equal values at successive vertices, hence is constant on each positive-conductance component; it is zero on any such component meeting because there. Conversely, if these componentwise conditions hold, every edge with has zero endpoint difference, while an edge with contributes zero regardless of its difference. Thus and .
Suppose . From [F1], every column of sums to zero, so and therefore . Multiplying on the left by gives . The second term vanishes, and the first is the derivative of , proving the balance law and its homogeneous conservation case.
Depends on
Used by
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Sources
- Fan R. K. Chung, Spectral Graph Theory, Chapter 1: Eigenvalues and the Laplacian of a Graph, CBMS 92 (1997), complete chapter text (standard reference, not scraped)
- Ward Whitt, Continuous-Time Markov Chains, full lecture notes (2006) (standard reference, not scraped)