ON CONDITIONAL EDGE-CONNECTIVITY OF GRAPHS RegularGraphsconnectivityconditionalconnectivityhypercubesLet k and h be two integers, 0≤h徐俊明Acta Mathematicae Applicatae Sinica(English Series)
On restricted edge-connectivity of graphs Summary: This paper considers the concept of restricted edge-connectivity, and relates that to the edge-degree of a connected graph. The author gives some ... JM Xu,KL Xu - 《Discrete Mathematics》 被引量: 321发表: 2002年 Sufficient conditions for gr...
On the generalized (edge-)connectivity of graphs Xueliang Li,Yaping Mao,Yuefang Sun Full-Text Cite this paper Add to My Lib Abstract: The generalized $k$-connectivity $\kappa_k(G)$ of a graph $G$ was introduced by Chartrand et al. in 1984. It is natural to introduce the concept...
In this paper, the minimum degree of power graphs of certain classes of cyclic groups, abelian $p$-groups, dihedral groups and dicyclic groups are obtained. It is ascertained that the edge-connectivity and minimum degree of power graphs are equal, and consequently the minimum disconnecting sets...
Furthermore, the set of N(n) edge-disjoint paths need not consist of a path of length greater than four. The result is extended to all polytopes of diameter two and paths can be constructed there of provided the corresponding adjacency rules are known....
which is similar to graph generation in that graphs are also set objects. In this survey, we only focus on graph generation whose objective concerns with generation of both the nodes and edges matrices, whereas set generation typically does not consider edge features. Nevertheless, we recognize th...
Literature of Deep Learning for Graphs This is a paper list about deep learning for graphs. Sort by topic Sort by venue 1 Node Representation Learning 1.1 Unsupervised Node Representation Learning 1.2 Node Representation Learning in Heterogeneous Graphs 1.3 Node Representation Learning in Dynamic Graphs...
The complex networks exhibit significant heterogeneity in node connections, resulting in a few nodes playing critical roles in various scenarios, including decision-making, disease control, and population immunity. Therefore, accurately identifying these
Use G ko to compute the n × n z-score matrix Z comprising the z-score values of all (potential) edges. 3. Compute the n × n matrix Z c as the z-score calculated on the absolute value of the entries of the conditional correlation matrix C, and add a (minimal) offset...
edge, which not only utilized spatial information but also improved the segmentation accuracy. The above model improves the ability of the model to recognize the edge information of the object and effectively integrates the feature information of the high and low layers of the image.Another ...