Abstract:The subject of this paper are infinite, locally finite, vertex-transitive median graphs. It is shown that the finiteness of the -classes of such graphs does not guarantee finite blocks. Blocks become finite if, in addition, no finite sequence of -contractions produces new cutvertices. It is proved that there are only finitely many vertex-transitive median graphs of given finite degree with finite blocks. An infinite family of vertex-transitive median graphs with finite intransitive blocks is also constructed a… Show more
“…The pairwise kernel matrix can be interpreted as a weighted adjacency matrix of the Kronecker product graph [8] of the two graphs whose weighted adjacency matrices are the instance-wise kernel matrices. Therefore, we refer to this pairwise kernel as Kronecker kernel to distinguish from the one we will propose in the next section.…”
Section: Pairwise Classification Problem and The Pairwise Kernelmentioning
confidence: 99%
“…So, it is natural to imagine that we can design another pairwise kernel based on another kind of product graph. In this paper, we adopt another kind of product graph called Cartesian product graph [8]. Assume that we have two graphs G (1) and G (2) whose sets of nodes are V (1) and V (2) , respectively.…”
Section: Cartesian Kernel: a New Pairwise Kernelmentioning
Abstract. Pairwise classification has many applications including network prediction, entity resolution, and collaborative filtering. The pairwise kernel has been proposed for those purposes by several research groups independently, and become successful in various fields. In this paper, we propose an efficient alternative which we call Cartesian kernel. While the existing pairwise kernel (which we refer to as Kronecker kernel) can be interpreted as the weighted adjacency matrix of the Kronecker product graph of two graphs, the Cartesian kernel can be interpreted as that of the Cartesian graph which is more sparse than the Kronecker product graph. Experimental results show the Cartesian kernel is much faster than the existing pairwise kernel, and at the same time, competitive with the existing pairwise kernel in predictive performance. We discuss the generalization bounds by the two pairwise kernels by using eigenvalue analysis of the kernel matrices.
“…The pairwise kernel matrix can be interpreted as a weighted adjacency matrix of the Kronecker product graph [8] of the two graphs whose weighted adjacency matrices are the instance-wise kernel matrices. Therefore, we refer to this pairwise kernel as Kronecker kernel to distinguish from the one we will propose in the next section.…”
Section: Pairwise Classification Problem and The Pairwise Kernelmentioning
confidence: 99%
“…So, it is natural to imagine that we can design another pairwise kernel based on another kind of product graph. In this paper, we adopt another kind of product graph called Cartesian product graph [8]. Assume that we have two graphs G (1) and G (2) whose sets of nodes are V (1) and V (2) , respectively.…”
Section: Cartesian Kernel: a New Pairwise Kernelmentioning
Abstract. Pairwise classification has many applications including network prediction, entity resolution, and collaborative filtering. The pairwise kernel has been proposed for those purposes by several research groups independently, and become successful in various fields. In this paper, we propose an efficient alternative which we call Cartesian kernel. While the existing pairwise kernel (which we refer to as Kronecker kernel) can be interpreted as the weighted adjacency matrix of the Kronecker product graph of two graphs, the Cartesian kernel can be interpreted as that of the Cartesian graph which is more sparse than the Kronecker product graph. Experimental results show the Cartesian kernel is much faster than the existing pairwise kernel, and at the same time, competitive with the existing pairwise kernel in predictive performance. We discuss the generalization bounds by the two pairwise kernels by using eigenvalue analysis of the kernel matrices.
“…The main topic of this paper is the direct product (that is known also by many other names, see [6]). It is the most natural graph product in the sense that each edge of G × H projects to an edge in both factors G and H .…”
Section: Introductionmentioning
confidence: 99%
“…This is not so rare and also in this work we show that it is enough for one factor to be a distance magic graph with one additional property and then the product with any regular graph will result in a distance magic graph. More details about the direct product and products in general can be found in the book [6].…”
Let G = (V, E) be a graph of order n. A distance magic labeling of G is a bijection : V → {1, . . . , n} for which there exists a positive integer k such thatWe introduce a natural subclass of distance magic graphs. For this class we show that it is closed for the direct product with regular graphs and closed as a second factor for lexicographic product with regular graphs. In addition, we characterize distance magic graphs among direct product of two cycles.
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