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A characterization of the smallest eigenvalue of a graph. (English) Zbl 0792.05096
Summary: It is well known that the smallest eigenvalue of the adjacency matrix of a connected \(d\)-regular graph is at least \(-d\) and is strictly greater than \(-d\) if the graph is not bipartite. More generally, for any connected graph \(G=(V,E)\), consider the matrix \(Q=D+A\) where \(D\) is the diagonal matrix of degrees in the graph \(G\) and \(A\) is the adjacency matrix of \(G\). Then \(Q\) is positive semidefinite, and the smallest eigenvalue of \(Q\) is 0 if and only if \(G\) is bipartite. We will study the separation of this eigenvalue from 0 in terms of the following measure of nonbipartiteness of \(G\). For any \(S \subseteq V\), we denote by \(e_{\min} (S)\) the minimum number of edges that need to be removed from the induced subgraph on \(S\) to make it bipartite. Also, we denote by \(\text{cut} (S)\) the set of edges with one end in \(S\) and the other in \(V-S\). We define the parameter \(\psi\) as \[ \psi=\min_{S \subseteq V} {e_{\min} (S)+| \text{cut} (S) | \over | S |}. \] The parameter \(\psi\) is a measure of the nonbipartiteness of the graph \(G\). We will show that the smallest eigenvalue of \(Q\) is bounded above and below by functions of \(\psi\). For \(d\)-regular graphs, this characterizes the separation of the smallest eigenvalue of the adjacency matrix from \(- d\). These results can be easily extended to weighted graphs.

MSC:
05C50 Graphs and linear algebra (matrices, eigenvalues, etc.)
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