Oriented incidence matrix ($D_{e,i}=1,D_{e,j}=-1$)

An oriented incidence matrix records an arbitrarily chosen orientation of each edge of a graph. Use one row per edge, with $+1$ and $-1$ at its endpoints and zeros elsewhere; the transpose convention is also common. The product $D\theta$ records endpoint differences. For a connected graph, $\ker D=\mathrm{span}\{1\}$, since zero difference propagates along every graph path.

For a connected graph, the Moore-Penrose inverse of its oriented incidence matrix satisfies $D^{†}D=I-{\Pi }_1$, because this product is the orthogonal projection onto $(\ker D{)}^{\perp }$. Thus every signal decomposes into its constant mean and $D^{†}D\theta$. Writing $L={\max }_e\Vert (D^{†}{)}_{\cdot ,e}{\Vert }_2$, a sub-Gaussian random vector noise gives a simultaneous bound on $\Vert (D^{†}{)}^{\top }z{\Vert }_{\infty }$ with scale $L\sqrt{\log (m/\delta )}$.