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Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

open access: yesRemote Sensing, 2014
In computer science, dependence analysis determines whether or not it is safe to parallelize statements in programs. In dealing with the data-intensive and computationally intensive spatial operations in processing massive volumes of geometric features ...
Xiaochen Kang, Xiangguo Lin
doaj   +1 more source

OBDD-Based Representation of Interval Graphs [PDF]

open access: yes, 2013
A graph $G = (V,E)$ can be described by the characteristic function of the edge set $\chi_E$ which maps a pair of binary encoded nodes to 1 iff the nodes are adjacent. Using \emph{Ordered Binary Decision Diagrams} (OBDDs) to store $\chi_E$ can lead to a (
B. Bollig   +22 more
core   +1 more source

Graph Structures in Bipolar Neutrosophic Environment

open access: yesMathematics, 2017
A bipolar single-valued neutrosophic (BSVN) graph structure is a generalization of a bipolar fuzzy graph. In this research paper, we present certain concepts of BSVN graph structures.
Muhammad Akram   +2 more
doaj   +1 more source

Certain operations on interval-valued picture fuzzy graphs with application

open access: yesInternational Journal of Mathematics for Industry, 2023
Graph theory has various applications in computer science, such as image segmentation, clustering, data mining, image capturing, and networking. Fuzzy graph (FG) theory has been widely adopted to handle uncertainty in graph-related problems.
Biswajit Das Adhikari   +3 more
doaj   +1 more source

Optimization of strip-layout using graph-theoretic methodology for stamping operations on progressive die: a case study

open access: yesInternational Journal for Simulation and Multidisciplinary Design Optimization, 2021
The design of the progressive die stamping process is optimized through minimizing the number of die stamping stations in the strip layout to reduce the die cost.
Aly Shady   +3 more
doaj   +1 more source

Learning flexible representations of stochastic processes on graphs

open access: yes, 2018
Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear ...
Balan, Radu   +2 more
core   +1 more source

J-coloring of graph operations [PDF]

open access: yesActa Universitatis Sapientiae, Informatica, 2019
Abstract A vertex v of a given graph is said to be in a rainbow neighbourhood of G if every color class of G consists of at least one vertex from the closed neighbourhood N[v]. A maximal proper coloring of a graph G is a J-coloring if and only if every vertex of G belongs to a rainbow neighbourhood of G.
Naduvath Sudev, Kok Johan
openaire   +3 more sources

Weak Total Resolvability In Graphs

open access: yesDiscussiones Mathematicae Graph Theory, 2016
A vertex v ∈ V (G) is said to distinguish two vertices x, y ∈ V (G) of a graph G if the distance from v to x is di erent from the distance from v to y.
Casel Katrin   +3 more
doaj   +1 more source

A Multiplicative Version of Forgotten Topological Index [PDF]

open access: yesMathematics Interdisciplinary Research, 2019
In this paper, we present upper bounds for the multiplicative forgotten topological index of several graph operations such as sum, Cartesian product, corona product, composition, strong product, disjunction and symmetric difference in terms of the F ...
Asghar Yousefi   +3 more
doaj   +1 more source

RAINBOW VERTEX-CONNECTION NUMBER ON COMB PRODUCT OPERATION OF CYCLE GRAPH (C_4) AND COMPLETE BIPARTITE GRAPH (K_(3,N))

open access: yesBarekeng, 2023
Rainbow vertex-connection number is the minimum colors assignment to the vertices of the graph, such that each vertex is connected by a path whose edges have distinct colors and is denoted by .
Nisky Imansyah Yahya   +3 more
doaj   +1 more source

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