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For a weighted and directed graph $G$ on $n$ vertices we define the Laplacian matrix by $L(G) = D(G)-A(G)$. Here $(i,j)$-th entry of ${A(G)}$ equals the weight $w_{ij}$ of the edge from $i$ to $j$ if there is one and zero if there is no edge from $i$ to $j$. Furthermore, $D(G)$ is the diagonal matrix, where the $(i,i)$-th entry of $D(G)$ equals $-\sum_{j=1}^{n} w_{ij}$ (after some ordering of the vertex set). We denote the eigenvalues of $L(G)$ by $\lambda_0 = 0, \lambda_1, \ldots$. We assume now that the graph $G$ has only positive edge weights is strongly connected, i.e., from each vertex we can reach every other vertex by using outgoing edges only, then the $\lambda_i$ all have positve real part for $i \neq 0$. We denote by $\lambda_1$ one of the eigenvalues with minimal positive real part (this choice may not be unique).

If we consider the $n$-dimensional hypercube graph $G$ (see http://en.wikipedia.org/wiki/Hypercube_graph for more information), consider each undirected edge as two edges (for both directions between two vertices) and define each edge weight as $1$, we obtain that the laplacian eigenvalues are $2i$ with multiplicity $\binom{n}{i}$ for $i=0,\ldots,n$. Hence, $\lambda_1 = 2$.

My question is now: Is there any $n$ and any labelling with positive weights for the edges of the $n$-dimensional hypercube graph $G$ (considered as directed and weighted graph), where for each vertex the sum over the outgoing respective incoming edge weights equals $n$ and the real part of $\lambda_1$ of $L(G)$ is bigger than 2 (the more the better)? And is there any estimate to these optimally large reals part of $\lambda_1$ as a function of $n$? Of course, it would be good to have a general method to construct such weights, but this would be only optional.

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The graph is symmetric enough that the "standard" weights are almost certainly optimal. For more, check out D. Jacobson and I. Rivin, Extremal metrics on graphs.

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