Showing papers on "Spectral graph theory published in 2022"

feGRASS: Fast and Effective Graph Spectral Sparsification for Scalable Power Grid Analysis

Abstract: Graph spectral sparsification aims to find a ultrasparse subgraph which can preserve the spectral properties of the original graph. The subgraph can be leveraged to construct a preconditioner to speed up the solution of the original graph’s Laplacian matrix. In this work, we propose feGRASS, a fast and effective graph spectral sparsification approach for the problem of large-scale power grid analysis and other problems with similar graphs. The proposed approach is based on two novel concepts: 1) effective edge weight and 2) spectral edge similarity. The former takes advantage of node degrees and breadth-first-search (BFS) distances, which leads to a scalable algorithm for generating low-stretch spanning trees (LSSTs). Then, the latter concept is leveraged during the recovery of spectrally critical off-tree edges to produce spectrally similar subgraphs. Compared with the most recent competitor [1] , the proposed approach is much faster for producing high-quality spectral sparsifiers. Extensive experimental results have been demonstrated to illustrate the superior efficiency of a preconditioned conjugate gradient (PCG) algorithm based on the proposed approach, for solving large power grid problems and many other real-world graph Laplacians. For instance, a power grid matrix with 60 million unknowns and 260 million nonzeros can be solved (at a 1E-3 accuracy level) within 196 s and 12 PCG iterations, on a single CPU core.

Hypergraphs with Edge-Dependent Vertex Weights: Spectral Clustering Based on the 1-Laplacian

Abstract: We propose a flexible framework for defining the 1-Laplacian of a hypergraph that incorporates edge-dependent vertex weights. These weights are able to reflect varying importance of vertices within a hyperedge, thus conferring the hypergraph model higher expressivity than homogeneous hypergraphs. We then utilize the eigenvector associated with the second smallest eigenvalue of the hypergraph 1-Laplacian to cluster the vertices. From a theoretical standpoint based on an adequately defined normalized Cheeger cut, this procedure is expected to achieve higher clustering accuracy than that based on the traditional Laplacian. Indeed, we confirm that this is the case using real-world datasets to demonstrate the effectiveness of the proposed spectral clustering approach. Moreover, we show that for a special case within our framework, the corresponding hypergraph 1-Laplacian is equivalent to the 1-Laplacian of a related graph, whose eigenvectors can be computed more efficiently, facilitating the adoption on larger datasets.

Graph Theory: A Lost Component For Development in Nigeria

Abstract: Graph theory is one of the neglected branches of mathematics in Nigeria but with the most applications in other fields of research. This article shows the paucity, importance, and necessity of graph theory in the development of Nigeria. The adjacency matrix and dual graph of the Nigeria map were presented. The graph spectrum and energies (graph energy and Laplacian energy) of the dual graph were computed. Then the chromatic number, maximum degree, minimum spanning tree, graph radius, and diameter, the Eulerian circuit and Hamiltonian paths from the dual graph were obtained and discussed.

A Spectral Representation of Networks: The Path of Subgraphs

Abstract: Network representation learning has played a critical role in studying networks. One way to study a graph is to focus on its spectrum, i.e., the eigenvalue distribution of its associated matrices. Recent advancements in spectral graph theory show that spectral moments of a network can be used to capture the network structure and various graph properties. However, sometimes networks with different structures or sizes can have the same or similar spectral moments, not to mention the existence of the cospectral graphs. To address such problems, we propose a 3D network representation that relies on the spectral information of subgraphs: the Spectral Path, a path connecting the spectral moments of the network and those of its subgraphs of different sizes. We show that the spectral path is interpretable and can capture relationship between a network and its subgraphs, for which we present a theoretical foundation. We demonstrate the effectiveness of the spectral path in applications such as network visualization and network identification.

Positive Consensus of Directed Multiagent Systems

Abstract: This technical note investigates the positive consensus problem of multiagent systems with directed communication topologies, where all the agents have identical continuous-time positive linear dynamics. Existing works of such a problem mainly focus on the case, where networked communication topologies are of either undirected and incomplete graphs, or strongly connected directed graphs. In contrast to them, we study this problem in which the communication topologies of the multiagent system are described by directed graphs each containing a spanning tree, which is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains. Based on the existing results in spectral graph theory and positive linear systems theory, several necessary, and sufficient conditions on positive consensus of the directed multiagent system are derived through using linear matrix inequality techniques. A primal-dual iterative algorithm is developed for the computation of solutions. Finally, several numerical simulations are provided to illustrate the effectiveness of the proposed theoretical results.

Graphs, Simplicial Complexes and Hypergraphs: Spectral Theory and Topology

Abstract: In this chapter we discuss the spectral theory of discrete structures such as graphs, simplicial complexes and hypergraphs. We focus, in particular, on the corresponding Laplace operators. We present the theoretical foundations, but we also discuss the motivation to model and study real data with these tools.

Spectral Graph Theory-Based Recovery Method for Missing Harmonic Data

Abstract: In large-scale applications, parts of harmonic data are inevitably lost during transmission. This study presents an approach for the recovery of missing harmonic data based on the spectral graph theory. The proposed methodology involves graph theory for constructing a Laplacian matrix and a graph signal reconstruction function, the merging K -means algorithm for building a priori information model, and the accelerated segmentation Bregman iterative algorithm for solving the reconstruction function. Compared with existing methods on data recovery in power systems, the method maintains a good recovery accuracy when the data correlation of measurement units is low and the prior information is little. The proposed method has good anti-noise performances and low computational complexity. The feasibility and accuracy of the proposed method are verified through simulation and field recorded data.

Generalizing p-Laplacian: spectral hypergraph theory and a partitioning algorithm

Abstract: Abstract For hypergraph clustering, various methods have been proposed to define hypergraph p -Laplacians in the literature. This work proposes a general framework for an abstract class of hypergraph p -Laplacians from a differential-geometric view. This class includes previously proposed hypergraph p -Laplacians and also includes previously unstudied novel generalizations. For this abstract class, we extend current spectral theory by providing an extension of nodal domain theory for the eigenvectors of our hypergraph p -Laplacian. We use this nodal domain theory to provide bounds on the eigenvalues via a higher-order Cheeger inequality. Following our extension of spectral theory, we propose a novel hypergraph partitioning algorithm for our generalized p -Laplacian. Our empirical study shows that our algorithm outperforms spectral methods based on existing p -Laplacians.

Laplacian matrix graph for anomaly target detection in hyperspectral images

Abstract: To improve the accuracy of abnormal target detection in hyperspectral images, an abnormal target detection method based on Laplacian matrix graph (LGD) is proposed. The method makes full use of the spatial and spectral information of hyperspectral abnormal targets by constructing the full-connection graph and the nearest neighbour matrix obtained by the Gaussian kernel function. In the graph, the total variation of the graph signal calculated by the Laplacian matrix is taken as the evaluation function to judge the abnormal target, so as to realize the detection of abnormal pixels. It avoids the matrix inversion calculation that must be carried out in the conventional detection algorithms and the complexity is reduced. Compared with other detection algorithms on three different data sets, the experiment results show that the proposed algorithm presents obvious advantages in detection accuracy and excellent utility in practice.

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

Abstract: We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors. Spectral conditioning allows for direct modeling of the global and local graph structure and helps to overcome the expressivity and mode collapse issues of one-shot graph generators. Our novel GAN, called SPECTRE, enables the one-shot generation of much larger graphs than previously possible with one-shot models. SPECTRE outperforms state-of-the-art deep autoregressive generators in terms of modeling fidelity, while also avoiding expensive sequential generation and dependence on node ordering. A case in point, in sizable synthetic and real-world graphs SPECTRE achieves a 4-to-170 fold improvement over the best competitor that does not overfit and is 23-to-30 times faster than autoregressive generators.

Biased random walk based graph neural network

Abstract: Graph neural networks (GNNs) are approaches that extend deep learning neural networks on graph data. Research on graph neural networks has made tremendous progress today. Graph neural networks are usually categorized as spectral-based models and spatial-based models. The spectral-based method has been widely recognized by the academic community due to its solid theoretical foundation. However, the existing spectral-based models induced by the Laplacian matrix usually cannot achieve satisfactory results in experiments due to their insufficient expressive ability. We theoretically derive an unbiased Laplacian matrix based on biased random walks. As a graph shift operator, it is more general than unbiased Laplacian. Based on biased Laplacian, we propose a more powerful spectral-based graph neural network BiGNN. And it achieves better simulation results than traditional spectral-based graph neural networks on Cora, Citeseer and PubMed datasets.

A note on Spectral Analysis of Quantum graphs

Abstract: We provide an introductory review of some topics in spectral theory of Laplacians on metric graphs. We focus on three different aspects: the trace formula, the self-adjointness problem and connections between Laplacians on metric graphs and discrete graph Laplacians.

Signed graphs whose all Laplacian eigenvalues are main

Abstract: For a graph G we consider the problem of the existence of a switching equivalent signed graph with Laplacian eigenvalues that are all main and the problem of determination of all switching equivalent signed graphs with this spectral property. Using a computer search we confirm that apart from K2 every connected graph with at most 7 vertices switches to at least one signed graph with the required property. This fails to hold for exactly 22 connected graphs with 8 vertices. If G is a cograph without repeated eigenvalues, then we give an iterative solution for the latter problem and the complete solution in the particular case when G is a threshold graph. The first problem is resolved positively for a particular class of chain graphs. The obtained results are applicable in control theory for generating controllable signed graphs based on Laplacian dynamics.

Iterated line graphs with only negative eigenvalues $-2$, their complements and energy

Abstract: The graphs with all equal negative or positive eigenvalues are special kind in the spectral graph theory. In this article, several iterated line graphs $\mathcal{L}^k(G)$ with all equal negative eigenvalues $-2$ are characterized for $k\ge 1$ and their energy consequences are presented. Also, the spectra and the energy of complement of these graphs are obtained, interestingly they have exactly two positive eigenvalues with different multiplicities. Moreover, we characterize a large class of equienergetic graphs which generalize some of the existing results. There are two different quotient matrices defined for an equitable partition of $H$-join (generalized composition) of regular graphs to find the spectrum (partial) of adjacency matrix, Laplacian matrix and signless Laplacian matrix, it has been proved that these two quotient matrices give the same respective spectrum of graphs.

Spektrum Laplace pada graf kincir angin berarah (Q_k^3)

Abstract: Suppose that 0 = µ0 ≤ µ1 ≤ ... ≤ µn-1 are eigen values of a Laplacian matrix graph with n vertices and m(µ0), m(µ1), …, m(µn-1) are the multiplicity of each µ, so the Laplacian spectrum of a graph can be expressed as a matrix 2 × n whose line elements are µ0, µ1, …, µn-1 for the first row, and m(µ0), m(µ1), …, m(µn-1) for the second row. In this paper, we will discuss Laplacian spectrum of the directed windmill graph () with k ≥ 1. The determination of the Laplacian spectrum in this study is to determine the characteristic polynomial of the Laplacian matrix from the directed windmill graph () with k ≥ 1. Keywords: Characteristic polynomial, directed windmill graph, Laplacian matrix, Laplacian spectrum.MSC2020 :05C50

The spectrum on prism graph using circulant matrix

Abstract: Spectral graph theory discusses about the algebraic properties of graphs based on the spectrum of a graph. This article investigated the spectrum of prism graph. The method used in this research is the circulant matrix. The results showed that prism graph P2,s is a regular graph of degree 3, for s odd and s ≥ 3, P2,s is a circulantt graph with regular spectrum.

Spectral Triadic Decompositions of Real-World Networks

Abstract: A fundamental problem in mathematics and network analysis is to find conditions under which a graph can be partitioned into smaller pieces. The most important tool for this partitioning is the Fiedler vector or discrete Cheeger inequality. These results relate the graph spectrum (eigenvalues of the normalized adjacency matrix) to the ability to break a graph into two pieces, with few edge deletions. An entire subfield of mathematics, called spectral graph theory, has emerged from these results. Yet these results do not say anything about the rich community structure exhibited by real-world networks, which typically have a significant fraction of edges contained in numerous densely clustered blocks. Inspired by the properties of real-world networks, we discover a new spectral condition that relates eigenvalue powers to a network decomposition into densely clustered blocks. We call this the \emph{spectral triadic decomposition}. Our relationship exactly predicts the existence of community structure, as commonly seen in real networked data. Our proof provides an efficient algorithm to produce the spectral triadic decomposition. We observe on numerous social, coauthorship, and citation network datasets that these decompositions have significant correlation with semantically meaningful communities.

Boundary Conditions Matter: On The Spectrum Of Infinite Quantum Graphs

Abstract: We develop a comprehensive spectral geometric theory for two distinguished self-adjoint realisations of the Laplacian, the so-called Friedrichs and Neumann extensions, on infinite metric graphs. We present a new criterion to determine whether these extensions have compact resolvent or not, leading to concrete examples where this depends on the chosen extension. In the case of discrete spectrum, under additional metric assumptions, we also extend known upper and lower bounds on Laplacian eigenvalues to metric graphs that are merely locally finite. Some of these bounds are new even on compact graphs.

The Power of Two Matrices in Spectral Algorithms

Abstract: Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms have been successfully used for graph partitioning, hidden clique recovery and graph coloring. In this paper, we study the power of spectral algorithms using two matrices in a graph partitioning problem. We use two different matrices resulting from two different encodings of the same graph and then combine the spectral information coming from these two matrices. We analyze a two-matrix spectral algorithm for the problem of identifying latent community structure in large random graphs. In particular, we consider the problem of recovering community assignments exactly in the censored stochastic block model, where each edge status is revealed independently with some probability. We show that spectral algorithms based on two matrices are optimal and succeed in recovering communities up to the information theoretic threshold. On the other hand, we show that for most choices of the parameters, any spectral algorithm based on one matrix is suboptimal. This is in contrast to our prior works (2022a, 2022b) which showed that for the symmetric Stochastic Block Model and the Planted Dense Subgraph problem, a spectral algorithm based on one matrix achieves the information theoretic threshold. We additionally provide more general geometric conditions for the (sub)-optimality of spectral algorithms.

Sign and Basis Invariant Networks for Spectral Graph Representation Learning

Abstract: We introduce SignNet and BasisNet -- new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if $v$ is an eigenvector then so is $-v$; and (ii) more general basis symmetries, which occur in higher dimensional eigenspaces with infinitely many choices of basis eigenvectors. We prove that under certain conditions our networks are universal, i.e., they can approximate any continuous function of eigenvectors with the desired invariances. When used with Laplacian eigenvectors, our networks are provably more expressive than existing spectral methods on graphs; for instance, they subsume all spectral graph convolutions, certain spectral graph invariants, and previously proposed graph positional encodings as special cases. Experiments show that our networks significantly outperform existing baselines on molecular graph regression, learning expressive graph representations, and learning neural fields on triangle meshes. Our code is available at https://github.com/cptq/SignNet-BasisNet .

Spectral identification of networks with generalized diffusive coupling

Abstract: Spectral network identification aims at inferring the eigenvalues of the Laplacian matrix of a network from measurement data. This allows to capture global information on the network structure from local measurements at a few number of nodes. In this paper, we consider the spectral network identification problem in the generalized setting of a vector-valued diffusive coupling. The feasibility of this problem is investigated and theoretical results on the properties of the associated generalized eigenvalue problem are obtained. Finally, we propose a numerical method to solve the generalized network identification problem, which relies on dynamic mode decomposition and leverages the above theoretical results.