Results 221 to 230 of about 112,189 (262)

Simple graphs in granular computing [PDF]

open access: yesInformation Sciences, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tommaso Gentile   +2 more
exaly   +4 more sources

Computing with graphs and graph transformations

Software: Practice and Experience, 1999
Many software applications require the construction and manipulation of graphs. In standard programming languages, this is accomplished using low-level mechanisms such as pointer manipulation or array indexing. In contrast, graph productions are a convenient high-level visual notation for coding graph modifications.
Dorothea Blostein, Andy Schürr
openaire   +2 more sources

Computability of graphs

Mathematical Logic Quarterly, 2020
AbstractWe consider topological pairs , , which have computable type, which means that they have the following property: if X is a computable topological space and a topological imbedding such that and are semicomputable sets in X, then is a computable set in X.
openaire   +1 more source

Parallel computations on a graph

Programming and Computer Software, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Igor B. Bourdonov   +2 more
openaire   +3 more sources

COMPUTATION GRAPHS

Kybernetes, 1976
The notion of a computation graph is introduced. A computation graph is a rooted, directed graph whose nodes are labelled by statements (instructions) to be executed. The motivation for developing computation graphs comes from a desire to represent programs by well‐defined, manipulable structures and to permit search (especially backtracking) to be a ...
openaire   +1 more source

Median graph computation for graph clustering

Soft Computing, 2005
In this paper, we are interested in the problem of graph clustering. We propose a new algorithm for computing the median of a set of graphs. The concept of median allows the extension of conventional algorithms such as the k-means to graph clustering, helping to bridge the gap between statistical and structural approaches to pattern recognition ...
Adel Hlaoui, Shengrui Wang
openaire   +2 more sources

Computing the Strength of a Graph

SIAM Journal on Computing, 1991
The paper presents an algorithm for computing the strength of a group, which improves the algorithm proposed by \textit{W. H. Cunningham} [J. Assoc. Comput. Mach. 32, 549-561 (1985; Zbl 0629.90034)] from \(O(V^ 4E)\) to \(O(V^ 3E)\) in complexity. The problem of computing the strength of a graph can be transformed into polymatroid form, and then it can
openaire   +1 more source

Computing with Condensed Graphs

2008 International Symposium on Parallel and Distributed Computing, 2008
Condensed Graphs provide a simple way of expressing complex dependencies in a program task graph or a work flow. In these graphs, nodes represent tasks and edges represent associated sequencing constraint. The sequence of task execution can be altered by altering the relationship between various nodes.
openaire   +1 more source

Parallel Computations in Graph Theory

SIAM Journal on Computing, 1975
In parallel computation two approaches are common; namely unbounded parallelism and bounded parallelism. In this paper both approaches will be considered. The problem of unbounded parallelism is studied in section II and some lower and upper bounds on different connectivity problems for directed and undirected graphs are presented.
Eshrat Reghbati, Derek G. Corneil
openaire   +2 more sources

The Various Graphs in Graph Computing

2019
The world is full of relationships, and graph is the most evident representation for them. With the increasing of data scale, graphs become larger and have encountered a new world of analyzing. What can we learn from a graph? How many kinds of graphs are there? How different is graph from one area to that from another? All these questions need answers,
Rujun Sun, Lufei Zhang
openaire   +1 more source

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