Applications of graph theory are primarily, but not exclusively, concerned with labeled graphs and various specializations of these.
Structures that can be represented as graphs are ubiquitous, and many problems of practical interest can be represented by graphs. The link structure of a website could be represented by a directed graph: the vertices are the web pages available at the website and a directed edge from page A to page B exists if and only if A contains a link to B. A similar approach can be taken to problems in travel, Biology, computer chip design, and many other fields. The development of algorithms to handle graphs is therefore of major interest in computer science. There, the transformation of graphs is often formalized and represented by graph rewrite systems. They are either directly used or properties of the rewrite systems(e.g. confluence) are studied.
A graph structure can be extended by assigning a weight to each edge of the graph. Graphs with weights, or weighted graphs, are used to represent structures in which pairwise connections have some numerical values. For example if a graph represents a road network, the weights could represent the length of each road. A digraph with weighted edges in the context of graph theory is called a network.
Networks have many uses in the practical side of graph theory, network analysis (for example, to model and analyze traffic networks). Within network analysis, the definition of the term "network" varies, and may often refer to a simple graph.
Many applications of graph theory exist in the form of network analysis. These split broadly into three categories. Firstly, analysis to determine structural properties of a network, such as the distribution of vertex degrees and the diameter of the graph. A vast number of graph measures exist, and the production of useful ones for various domains remains an active area of research. Secondly, analysis to find a measurable quantity within the network, for example, for a transportation network, the level of vehicular flow within any portion of it. Thirdly, analysis of dynamical properties of networks.
Graph theory is also used to study molecules in chemistry and physics. In condensed matter physics, the three dimensional structure of complicated simulated atomic structures can be studied quantitatively by gathering statistics on graph-theoretic properties related to the topology of the atoms. For example, Franzblau's shortest-path (SP) rings. In chemistry a graph makes a natural model for a molecule, where vertices represent atoms and edges bonds. This approach is especially used in computer processing of molecular structures, ranging from chemical editors to database searching.
Graph theory is also widely used in sociology as a way, for example, to measure actors' prestige or to explore diffusion mechanisms, notably through the use of social network analysis software.
Likewise, graph theory is useful in biology and conservation efforts where a vertex can represent regions where certain species exist (or habitats) and the edges represent migration paths, or movement between the regions. This information is important when looking at breeding patterns or tracking the spread of disease, parasites or how changes to the movement can affect other species.
On a computer - The file system uses a tree architecture, a menu system also has the same idea of selecting progressive layers of options.
Indexes in a book have a shallow tree structure - letter/subjects begining with that letter. Contents probably have a better layout - the different levels of chapters etc.
Main tree use are
1- searching.
2-reduce the length through the huffman tree.
3-in displaying the data of a company of an annual year.
include the exampal in the anser of last in the difenation of the anser under stand very will in the topic short cut in the anser
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The data structures are user defined data types specifically created for the manipulation of data in a predefined manner. Examples of data structures would be stacks,queues,trees,graphs and even arrays(also reffered as data structure)
Accessing data by address. Some data-structures, like lists and trees, are usually implemented using pointers.
Tree, Graphs are the types of nonlinear data structure.
The latter isn't primitive. Most likely it means 'non trivial', 'adaptive' or 'sophisticated'.
A record is a compound data structure composed of heterogeneous fields. The memory layout of an individual record is linear insofar as the fields are allocated contiguously, however a group of records is not necessarily linear. It all depends upon how the records are linked together and that's ultimately defined by the data container. Generally speaking, arrays and lists are linear data structures while graphs and networks are non-linear.
Bar graph - "talaang bar" or "areglong bar" Line graph - "graing linya" or "larawang linya" Pie graph - "bilog na grap" or "parihabaing grap"
The data structures are user defined data types specifically created for the manipulation of data in a predefined manner. Examples of data structures would be stacks,queues,trees,graphs and even arrays(also reffered as data structure)
bar graphs use categorical data
Data structures could be used to implement an efficient database. Linked lists for example will optimize insertion and deletion for ordered lists.
line graphs are usually the most best way to present data. sometimes i use pie graphs or bar graphs, but usually line graphs are the most meaningful.
They both show a set of data. Line graphs show data over time. Pie graphs show percentages in data.
Graphs visualize data allowing the brain to interpret a large data set quickly and infer trends.
line graphs, bar graphs,and circle
bar graphs are for measuring points of data.
graphs are to compare and contrast data
You can use pie charts, line graphs, bar graphs and scatter point graphs to present data.
Bar graphs can compare two sets of data, as well as line graphs and circle graphs. To better improve my answer, double line graphs and double bar graphs compare two sets of data. Circle graphs cannot however, because they compare parts of a whole instead of, as a bar graph would, the amount of something. A circle graph is also incapable of showing data growth over a period of time, as line graphs do. All in all, circle graphs cannot compare to sets of data, and bar graphs and line graphs must be doubled to do so.