Spectral graph theory

About: Spectral graph theory is a research topic. Over the lifetime, 1334 publications have been published within this topic receiving 77373 citations.

A Topological Investigation of Power Flow

Abstract: This paper combines the fundamentals of an electrical grid, such as flow allocation according to Kirchhoff's laws and the effect of transmission line reactances with spectral graph theory, and expresses the linearized power flow behaviour in slack-bus independent weighted graph matrices to assess the relation between the topological structure and the physical behaviour of a power grid. Based on the pseudoinverse of the weighted network Laplacian, the paper further analytically calculates the effective resistance (Thevenin) matrix and the sensitivities of active power flows to the changes in network topology by means of transmission line removal and addition. Numerical results for the IEEE 118-bus power system are demonstrated to identify the critical components to cascading failures, node isolation, and Braess’ paradox in a power grid.

On graph partitioning, spectral analysis, and digital mesh processing

Abstract: Partitioning is a fundamental operation on graphs. In this paper we briefly review the basic concepts of graph partitioning and its relationship to digital mesh processing. We also elaborate on the connection between graph partitioning and spectral graph theory. Applications in computer graphics are described.

Edge connectivity and the spectral gap of combinatorial and quantum graphs

Abstract: We derive a number of upper and lower bounds for the first nontrivial eigenvalue of Laplacians on combinatorial and quantum graph in terms of the edge connectivity, i.e. the minimal number of edges which need to be removed to make the graph disconnected. On combinatorial graphs, one of the bounds corresponds to a well-known inequality of Fiedler, of which we give a new variational proof. On quantum graphs, the corresponding bound generalizes a recent result of Band and Levy. All proofs are general enough to yield corresponding estimates for the p-Laplacian and allow us to identify the minimizers. Based on the Betti number of the graph, we also derive upper and lower bounds on all eigenvalues which are 'asymptotically correct', i.e. agree with the Weyl asymptotics for the eigenvalues of the quantum graph. In particular, the lower bounds improve the bounds of Friedlander on any given graph for all but finitely many eigenvalues, while the upper bounds improve recent results of Ariturk. Our estimates are also used to derive bounds on the eigenvalues of the normalized Laplacian matrix that improve known bounds of spectral graph theory.

An iterated graph laplacian approach for ranking on manifolds

Abstract: Ranking is one of the key problems in information retrieval. Recently, there has been significant interest in a class of ranking algorithms based on the assumption that data is sampled from a low dimensional manifold embedded in a higher dimensional Euclidean space.In this paper, we study a popular graph Laplacian based ranking algorithm [23] using an analytical method, which provides theoretical insights into the ranking algorithm going beyond the intuitive idea of "diffusion." Our analysis shows that the algorithm is sensitive to a commonly used parameter due to the use of symmetric normalized graph Laplacian. We also show that the ranking function may diverge to infinity at the query point in the limit of infinite samples. To address these issues, we propose an improved ranking algorithm on manifolds using Green's function of an iterated unnormalized graph Laplacian, which is more robust and density adaptive, as well as pointwise continuous in the limit of infinite samples.We also for the first time in the ranking literature empirically explore two variants from a family of twice normalized graph Laplacians. Experimental results on text and image data support our analysis, which also suggest the potential value of twice normalized graph Laplacians in practice.

On two conjectures regarding an inverse eigenvalue problem for acyclic symmetric matrices

Abstract: For a given acyclic graph G, an important problem is to characterize all of the eigenvalues over all symmetric matrices with graph G. Of particular interest is the connection between this standard inverse eigenvalue problem and describing all the possible associated ordered multiplicity lists, along with determining the minimum number of distinct eigenvalues for a symmetric matrix with graph G. In this note two important open questions along these lines are resolved, both in the negative.