A weighted graph is a graph that has a numeric label associated with each edge, called the weight of edge. In many applications, the edge weights are usually represented by nonnegative integers or square matrices. The...A weighted graph is a graph that has a numeric label associated with each edge, called the weight of edge. In many applications, the edge weights are usually represented by nonnegative integers or square matrices. The weighted signless Laplacian matrix of a weighted graph is defined as the sum of adjacency matrix and degree matrix of same weighted graph. In this paper, a brief overview of the notation and concepts of weighted graphs that will be used throughout this study is given. In Section 2, the weighted signless Laplacian matrix of simple connected weighted graphs is considered, some upper bounds for the spectral radius of the weighted signless Laplacian matrix are obtained and some results on weighted and unweighted graphs are found.展开更多
Let G be a simple graph and let Q（G） be the signless Laplacian matrix of G. In this paper we obtain some results on the spectral perturbation of the matrix Q（G） under an edge addition or an edge contraction.
In this paper, we found the bounds of the extreme eigenvalues of normalized Laplacian matrices and signless Laplacian matrices by using their traces. In addition, we found the bounds for k-th eigenvalues of normalized...In this paper, we found the bounds of the extreme eigenvalues of normalized Laplacian matrices and signless Laplacian matrices by using their traces. In addition, we found the bounds for k-th eigenvalues of normalized Laplacian matrix and signless Laplacian matrix.展开更多
This paper proposes an inner product Laplacian embedding algorithm based on semi-definite programming, named as IPLE algorithm. The new algorithm learns a geodesic distance-based kernel matrix by using semi-definite p...This paper proposes an inner product Laplacian embedding algorithm based on semi-definite programming, named as IPLE algorithm. The new algorithm learns a geodesic distance-based kernel matrix by using semi-definite programming under the constraints of local contraction. The criterion function is to make the neighborhood points on manifold as close as possible while the geodesic distances between those distant points are preserved. The IPLE algorithm sufficiently integrates the advantages of LE, ISOMAP and MVU algorithms. The comparison experiments on two image datasets from COIL-20 images and USPS handwritten digit images are performed by applying LE, ISOMAP, MVU and the proposed IPLE. Experimental results show that the intrinsic low-dimensional coordinates obtained by our algorithm preserve more information according to the fraction of the dominant eigenvalues and can obtain the better comprehensive performance in clustering and manifold structure.展开更多
The signless Laplacian matrix of a graph is the sum of its diagonal matrix of vertex degrees and its adjacency matrix. Li and Feng gave some basic results on the largest eigenvalue and characteristic polynomial of adj...The signless Laplacian matrix of a graph is the sum of its diagonal matrix of vertex degrees and its adjacency matrix. Li and Feng gave some basic results on the largest eigenvalue and characteristic polynomial of adjacency matrix of a graph in 1979. In this paper, we translate these results into the signless Laplacian matrix of a graph and obtain the similar results.展开更多
Suppose that the vertex set of a graph G is V(G) ={v1,v2,...,vn}.The transmission Tr(vi) (or Di) of vertex vi is defined to be the sum of distances from vi to all other vertices.Let Tr(G) be the n × n diagonal ma...Suppose that the vertex set of a graph G is V(G) ={v1,v2,...,vn}.The transmission Tr(vi) (or Di) of vertex vi is defined to be the sum of distances from vi to all other vertices.Let Tr(G) be the n × n diagonal matrix with its (i,i)-entry equal to TrG(vi).The distance signless Laplacian spectral radius of a connected graph G is the spectral radius of the distance signless Laplacian matrix of G,defined as L(G) =Tr(G) + D(G),where D(G) is the distance matrix of G.In this paper,we give a lower bound on the distance signless Laplacian spectral radius of graphs and characterize graphs for which these bounds are best possible.We obtain a lower bound on the second largest distance signless Laplacian eigenvalue of graphs.Moreover,we present lower bounds on the spread of distance signless Laplacian matrix of graphs and trees,and characterize extremal graphs.展开更多
Let G be a simple connected graph with n vertices.The transmission Tv of a vertex v is defined to be the sum of the distances from v to all other vertices in G,that is,T_(v)=Σ_(u)∈Vd_(uv),where duv denotes the dista...Let G be a simple connected graph with n vertices.The transmission Tv of a vertex v is defined to be the sum of the distances from v to all other vertices in G,that is,T_(v)=Σ_(u)∈Vd_(uv),where duv denotes the distance between u and v.Let T_(1),…,T_(n)be the transmission sequence of G.Let D=(dij)_(n×n)be the distance matrix of G,and T be the transmission diagonal matrix diag(T_(1),…,T_(n)).The matrix Q(G)=T+D is called the distance signless Laplacian of G.In this paper,we provide the distance signless Laplacian spectrum of complete k-partite graph,and give some sharp lower and upper bounds on the distance signless Laplacian spectral radius q(G).展开更多
文摘A weighted graph is a graph that has a numeric label associated with each edge, called the weight of edge. In many applications, the edge weights are usually represented by nonnegative integers or square matrices. The weighted signless Laplacian matrix of a weighted graph is defined as the sum of adjacency matrix and degree matrix of same weighted graph. In this paper, a brief overview of the notation and concepts of weighted graphs that will be used throughout this study is given. In Section 2, the weighted signless Laplacian matrix of simple connected weighted graphs is considered, some upper bounds for the spectral radius of the weighted signless Laplacian matrix are obtained and some results on weighted and unweighted graphs are found.
基金Supported by the National Natural Science Foundation of China(11071002)the Anhui Natural ScienceFoundation of China(11040606M14)NSF of Department of Education of Anhui Province(KJ2011A195)
文摘Let G be a simple graph and let Q（G） be the signless Laplacian matrix of G. In this paper we obtain some results on the spectral perturbation of the matrix Q（G） under an edge addition or an edge contraction.
文摘In this paper, we found the bounds of the extreme eigenvalues of normalized Laplacian matrices and signless Laplacian matrices by using their traces. In addition, we found the bounds for k-th eigenvalues of normalized Laplacian matrix and signless Laplacian matrix.
文摘This paper proposes an inner product Laplacian embedding algorithm based on semi-definite programming, named as IPLE algorithm. The new algorithm learns a geodesic distance-based kernel matrix by using semi-definite programming under the constraints of local contraction. The criterion function is to make the neighborhood points on manifold as close as possible while the geodesic distances between those distant points are preserved. The IPLE algorithm sufficiently integrates the advantages of LE, ISOMAP and MVU algorithms. The comparison experiments on two image datasets from COIL-20 images and USPS handwritten digit images are performed by applying LE, ISOMAP, MVU and the proposed IPLE. Experimental results show that the intrinsic low-dimensional coordinates obtained by our algorithm preserve more information according to the fraction of the dominant eigenvalues and can obtain the better comprehensive performance in clustering and manifold structure.
基金Foundation item： the National Natural Science Foundation of China （No. 10871204） Graduate Innovation Foundation of China University of Petroleum （No. S2008-26）.
文摘The signless Laplacian matrix of a graph is the sum of its diagonal matrix of vertex degrees and its adjacency matrix. Li and Feng gave some basic results on the largest eigenvalue and characteristic polynomial of adjacency matrix of a graph in 1979. In this paper, we translate these results into the signless Laplacian matrix of a graph and obtain the similar results.
基金The authors are grateful to the two anonymous referees for their careful reading of this paper and strict criticisms, constructive corrections, and valuable comments on this paper, which have considerably improved the presentation of this paperThe first author was supported by the National Research Foundation of the Korean government with grant No. 2017R1D1A1B03028642+2 种基金The second author was supported by the National Natural Science Foundation of China (Grant No. 11771141)the Fundamental Research Fund for the Central Universities (No. 222201714049)The third author was supported by the National Natural Science Foundation of China (Grant No. 11371372).
文摘Suppose that the vertex set of a graph G is V(G) ={v1,v2,...,vn}.The transmission Tr(vi) (or Di) of vertex vi is defined to be the sum of distances from vi to all other vertices.Let Tr(G) be the n × n diagonal matrix with its (i,i)-entry equal to TrG(vi).The distance signless Laplacian spectral radius of a connected graph G is the spectral radius of the distance signless Laplacian matrix of G,defined as L(G) =Tr(G) + D(G),where D(G) is the distance matrix of G.In this paper,we give a lower bound on the distance signless Laplacian spectral radius of graphs and characterize graphs for which these bounds are best possible.We obtain a lower bound on the second largest distance signless Laplacian eigenvalue of graphs.Moreover,we present lower bounds on the spread of distance signless Laplacian matrix of graphs and trees,and characterize extremal graphs.
基金supported by the National Natural Science Foundation of China(Grant Nos.11801144,11701148)the Natural Science Foundation of Education Ministry of Henan Province(18B110005).
文摘Let G be a simple connected graph with n vertices.The transmission Tv of a vertex v is defined to be the sum of the distances from v to all other vertices in G,that is,T_(v)=Σ_(u)∈Vd_(uv),where duv denotes the distance between u and v.Let T_(1),…,T_(n)be the transmission sequence of G.Let D=(dij)_(n×n)be the distance matrix of G,and T be the transmission diagonal matrix diag(T_(1),…,T_(n)).The matrix Q(G)=T+D is called the distance signless Laplacian of G.In this paper,we provide the distance signless Laplacian spectrum of complete k-partite graph,and give some sharp lower and upper bounds on the distance signless Laplacian spectral radius q(G).
基金Supported by National Natural Science Foundation of China(11071002)NFS of Anhui Province(11040606M14)NSF of Department of Education of Anhui Province(KJ2011A195,KJ2010B136)
