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Minimum k-Path Vertex Cover in Cartesian Product Graphs
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作者 Huiling YIN Binbin HAO +1 位作者 Xiaoyan SU Jingrong CHEN 《Journal of Mathematical Research with Applications》 CSCD 2021年第4期340-348,共9页
For the subset S■V(G), if every path with k vertices in a graph G contains at least one vertex from S, we call that S is a k-path vertex cover set of the graph G. Obviously, the subset is not unique. The cardinality ... For the subset S■V(G), if every path with k vertices in a graph G contains at least one vertex from S, we call that S is a k-path vertex cover set of the graph G. Obviously, the subset is not unique. The cardinality of the minimum k-path vertex cover set of a graph G is called the k-path vertex cover number, we denote it by ψk(G). In this paper, a lower or upper bound of ψk for some Cartesian product graphs is presented. 展开更多
关键词 k-path vertex cover cartesian product graphs BOUND
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Outer-Independent Roman Domination on Cartesian Product of Paths
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作者 Junzhe GUO Hong GAO Yuansheng YANG 《Journal of Mathematical Research with Applications》 2025年第1期11-19,共9页
Outer-independent Roman domination on graphs originates from the defensive strategy of Ancient Rome,which is that if any city without an army is attacked,a neighboring city with two armies could mobilize an army to su... Outer-independent Roman domination on graphs originates from the defensive strategy of Ancient Rome,which is that if any city without an army is attacked,a neighboring city with two armies could mobilize an army to support it and any two cities that have no army cannot be adjacent.The outer-independent Roman domination on graphs is an attractive topic in graph theory,and the definition is described as follows.Given a graph G=(V,E),a function f:V(G)→{0,1,2}is an outer-independent Roman dominating function(OIRDF)if f satisfies that every vertex v∈V with f(v)=0 has at least one adjacent vertex u∈N(v)with f(u)=2,where N(v)is the open neighborhood of v,and the set V0={v|f(v)=0}is an independent set.The weight of an OIRDF f is w(f)=∑_(v∈V)f(v).The value of minf w(f)is the outerindependent Roman domination number of G,denoted asγoiR(G).This paper is devoted to the study of the outer-independent Roman domination number of the Cartesian product of paths P_(n)□P_(m).With the help of computer,we find some recursive OIRDFs and then we present an upper bound ofγoiR(P_(n)□P_(m)).Furthermore,we prove the lower bound ofγoiR(P_(n)□P_(m))(n≤3)is equal to the upper bound.Hence,we achieve the exact value ofγoiR(P_(n)□P_(m))for n≤3 and the upper bound ofγoiR(P_(n)□P_(m))for n≥4. 展开更多
关键词 Roman domination outer-independent Roman domination cartesian product graphs PATHS
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The Path-Positive Property on the Products of Graphs
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作者 连广昌 《Journal of Southeast University(English Edition)》 EI CAS 1998年第2期130-134,共5页
The products of graphs discussed in this paper are the following four kinds: the Cartesian product of graphs, the tensor product of graphs, the lexicographic product of graphs and the strong direct product of graphs. ... The products of graphs discussed in this paper are the following four kinds: the Cartesian product of graphs, the tensor product of graphs, the lexicographic product of graphs and the strong direct product of graphs. It is proved that:① If the graphs G 1 and G 2 are the connected graphs, then the Cartesian product, the lexicographic product and the strong direct product in the products of graphs, are the path positive graphs. ② If the tensor product is a path positive graph if and only if the graph G 1 and G 2 are the connected graphs, and the graph G 1 or G 2 has an odd cycle and max{ λ 1μ 1,λ nμ m}≥2 in which λ 1 and λ n [ or μ 1 and μ m] are maximum and minimum characteristic values of graph G 1 [ or G 2 ], respectively. 展开更多
关键词 product of graphs path positive property cartesian product of graphs tensor product of graphs lexicographic product of graphs strong direct product of graphs
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Circular L(j,k)-labeling numbers of trees and products of graphs 被引量:3
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作者 吴琼 林文松 《Journal of Southeast University(English Edition)》 EI CAS 2010年第1期142-145,共4页
Let j, k and m be three positive integers, a circular m-L(j, k)-labeling of a graph G is a mapping f: V(G)→{0, 1, …, m-1}such that f(u)-f(v)m≥j if u and v are adjacent, and f(u)-f(v)m≥k if u and v are... Let j, k and m be three positive integers, a circular m-L(j, k)-labeling of a graph G is a mapping f: V(G)→{0, 1, …, m-1}such that f(u)-f(v)m≥j if u and v are adjacent, and f(u)-f(v)m≥k if u and v are at distance two,where a-bm=min{a-b,m-a-b}. The minimum m such that there exists a circular m-L(j, k)-labeling of G is called the circular L(j, k)-labeling number of G and is denoted by σj, k(G). For any two positive integers j and k with j≤k,the circular L(j, k)-labeling numbers of trees, the Cartesian product and the direct product of two complete graphs are determined. 展开更多
关键词 circular L(j k)-labeling number TREE cartesian product of graphs direct product of graphs
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Graph Laplacian Matrix Learning from Smooth Time-Vertex Signal 被引量:2
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作者 Ran Li Junyi Wang +2 位作者 Wenjun Xu Jiming Lin Hongbing Qiu 《China Communications》 SCIE CSCD 2021年第3期187-204,共18页
In this paper,we focus on inferring graph Laplacian matrix from the spatiotemporal signal which is defined as“time-vertex signal”.To realize this,we first represent the signals on a joint graph which is the Cartesia... In this paper,we focus on inferring graph Laplacian matrix from the spatiotemporal signal which is defined as“time-vertex signal”.To realize this,we first represent the signals on a joint graph which is the Cartesian product graph of the time-and vertex-graphs.By assuming the signals follow a Gaussian prior distribution on the joint graph,a meaningful representation that promotes the smoothness property of the joint graph signal is derived.Furthermore,by decoupling the joint graph,the graph learning framework is formulated as a joint optimization problem which includes signal denoising,timeand vertex-graphs learning together.Specifically,two algorithms are proposed to solve the optimization problem,where the discrete second-order difference operator with reversed sign(DSODO)in the time domain is used as the time-graph Laplacian operator to recover the signal and infer a vertex-graph in the first algorithm,and the time-graph,as well as the vertex-graph,is estimated by the other algorithm.Experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively infer meaningful time-and vertex-graphs from noisy and incomplete data. 展开更多
关键词 cartesian product graph discrete secondorder difference operator Gaussian prior distribution graph Laplacian matrix learning spatiotemporal smoothness time-vertex signal
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