Poster on Graph Terminologies
•Download as DOCX, PDF•
0 likes•254 views
This document discusses graph terminology and concepts. It defines a graph as composed of vertices and edges connecting the vertices. It provides examples of vertices and edges in a sample graph. It also defines key graph terminology including adjacent nodes, paths, simple paths, complete graphs, connected graphs, and subgraphs.
Report
Share
Report
Share
Recommended
Презентація дисертації
Презентація до захисту кандидатської дисертації "Концептуальні засади взаємодії органів державної влади та засобів масової інформації в умовах демократизації державного управління в Україні"
Формування та реалізація антикорупційної політики
Формування та реалізація антикорупційної політики: коментар статей 18-21 Закону «Про запобігання корупції»
Витяг з книги: Науково-практичний коментар до Закону України «Про запобігання корупції» / Наук. ред. Хавронюк М. І. – К.: Ваіте, 2018. – 472 с.
UIA Internet audience in Ukraine in 2019Q3
Дослідження інтернет-аудиторії в Україні за III квартал 2019 року.
Research of internet audience in Ukraine in 2019Q3
E seva in_andhra_pradesh
This document discusses eSeva, an e-governance program implemented in Andhra Pradesh, India. It provides key details about eSeva's implementation including:
- ESeva started in 2001 with 18 service centers providing 25 services to 500,000 customers per month, and by 2004 had expanded to 201 centers providing 55 services to 1.2 million customers per month.
- ESeva used a 3-tier architecture with government agencies, a central data center, and service centers connected by leased lines and ISDN for backup.
- ESeva was implemented through a public-private partnership model with private partners selected through competitive bids and paid per transaction completed.
Презентація Політико-правові вчення Київської Русі.pptx
Презентація про політико-правові вчення Київської Русі
Recommended
Презентація дисертації
Презентація до захисту кандидатської дисертації "Концептуальні засади взаємодії органів державної влади та засобів масової інформації в умовах демократизації державного управління в Україні"
Формування та реалізація антикорупційної політики
Формування та реалізація антикорупційної політики: коментар статей 18-21 Закону «Про запобігання корупції»
Витяг з книги: Науково-практичний коментар до Закону України «Про запобігання корупції» / Наук. ред. Хавронюк М. І. – К.: Ваіте, 2018. – 472 с.
UIA Internet audience in Ukraine in 2019Q3
Дослідження інтернет-аудиторії в Україні за III квартал 2019 року.
Research of internet audience in Ukraine in 2019Q3
E seva in_andhra_pradesh
This document discusses eSeva, an e-governance program implemented in Andhra Pradesh, India. It provides key details about eSeva's implementation including:
- ESeva started in 2001 with 18 service centers providing 25 services to 500,000 customers per month, and by 2004 had expanded to 201 centers providing 55 services to 1.2 million customers per month.
- ESeva used a 3-tier architecture with government agencies, a central data center, and service centers connected by leased lines and ISDN for backup.
- ESeva was implemented through a public-private partnership model with private partners selected through competitive bids and paid per transaction completed.
Презентація Політико-правові вчення Київської Русі.pptx
Презентація про політико-правові вчення Київської Русі
Graph theory concepts complex networks presents-rouhollah nabati
This document provides an introduction to network and social network analysis theory, including basic concepts of graph theory and network structures. It defines what a network and graph are, explains what network theory techniques are used for, and gives examples of real-world networks that can be represented as graphs. It also summarizes key graph theory concepts such as nodes, edges, walks, paths, cycles, connectedness, degree, and centrality measures.
Graph
Dr. M. Usha of the Department of Computer Science and Engineering at Velammal Engineering College gave a presentation on graphs. A graph is a non-linear data structure consisting of nodes and edges. Graphs can be used to represent networks and are applied in areas like social networks. Common graph terminology includes vertices, edges, paths, cycles, and graph representations like adjacency matrices and adjacency lists. Graph traversal algorithms like depth-first search and breadth-first search are used to systematically visit all nodes.
FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATION
Data mining algorithms are facing the challenge to deal with an increasing number of complex
objects. Graph is a natural data structure used for modeling complex objects. Frequent subgraph
mining is another active research topic in data mining . A graph is a general model to represent
data and has been used in many domains like cheminformatics and bioinformatics. Mining
patterns from graph databases is challenging since graph related operations, such as subgraph
testing, generally have higher time complexity than the corresponding operations on itemsets,
sequences, and trees. Many frequent subgraph Mining algorithms have been proposed. SPIN,
SUBDUE, g_Span, FFSM, GREW are a few to mention. In this paper we present a detailed
survey on frequent subgraph mining algorithms, which are used for knowledge discovery in
complex objects and also propose a frame work for classification of these algorithms. The
purpose is to help user to apply the techniques in a task specific manner in various application domains and to pave wave for further research.
Data Structures and Agorithm: DS 21 Graph Theory.pptx
Data Structures and Agorithm: DS 21 Graph Theory.pptx
Tn 110 lecture 8
Graph theory has many applications in modeling real-world networks like computer networks and chemical compounds. It studies graphs, which are collections of vertices connected by edges. A graph can be represented using an incidence matrix or adjacency list/matrix. Connectivity refers to reachability between vertices by traversing edges. The shortest path problem finds the minimum weight path between vertices in a weighted graph. Planar graphs can be drawn in a plane without edge crossings, and satisfy Euler's formula relating vertices, edges, and regions. Graph coloring assigns different colors to connected elements like vertices or edges.
On algorithmic problems concerning graphs of higher degree of symmetry
Since the ancient determination of the five platonic solids the study of symmetry and regularity has always
been one of the most fascinating aspects of mathematics. One intriguing phenomenon of studies in graph
theory is the fact that quite often arithmetic regularity properties of a graph imply the existence of many
symmetries, i.e. large automorphism group G. In some important special situation higher degree of
regularity means that G is an automorphism group of finite geometry. For example, a glance through the
list of distance regular graphs of diameter d < 3 reveals the fact that most of them are connected with
classical Lie geometry. Theory of distance regular graphs is an important part of algebraic combinatorics
and its applications such as coding theory, communication networks, and block design. An important tool
for investigation of such graphs is their spectra, which is the set of eigenvalues of adjacency matrix of a
graph. Let G be a finite simple group of Lie type and X be the set homogeneous elements of the associated
geometry. The complexity of computing the adjacency matrices of a graph Gr on the vertices X such that
Aut GR = G depends very much on the description of the geometry with which one starts. For example, we
can represent the geometry as the totality of 1 cosets of parabolic subgroups 2 chains of embedded
subspaces (case of linear groups), or totally isotropic subspaces (case of the remaining classical groups), 3
special subspaces of minimal module for G which are defined in terms of a G invariant multilinear form.
The aim of this research is to develop an effective method for generation of graphs connected with classical
geometry and evaluation of its spectra, which is the set of eigenvalues of adjacency matrix of a graph. The
main approach is to avoid manual drawing and to calculate graph layout automatically according to its
formal structure. This is a simple task in a case of a tree like graph with a strict hierarchy of entities but it
becomes more complicated for graphs of geometrical nature. There are two main reasons for the
investigations of spectra: (1) very often spectra carry much more useful information about the graph than a
corresponding list of entities and relationships (2) graphs with special spectra, satisfying so called
Ramanujan property or simply Ramanujan graphs (by name of Indian genius mathematician) are important
for real life applications (see [13]). There is a motivated suspicion that among geometrical graphs one
could find some new Ramanujan graphs.
Graph theory and its applications
This document is a project report submitted by S. Manikanta in partial fulfillment of the requirements for a Master of Science degree in Mathematics. The report discusses applications of graph theory. It provides an overview of graph theory concepts such as definitions of graphs, terminology used in graph theory, different types of graphs, trees and forests, graph isomorphism and operations, walks and paths in graphs, representations of graphs using matrices, applications of graphs in areas like computer science, fingerprint recognition, security, and more. The document also includes examples and illustrations to explain various graph theory concepts.
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
Since the ancient determination of the five platonic solids the study of symmetry and regularity has always
been one of the most fascinating aspects of mathematics. One intriguing phenomenon of studies in graph
theory is the fact that quite often arithmetic regularity properties of a graph imply the existence of many
symmetries, i.e. large automorphism group G. In some important special situation higher degree of
regularity means that G is an automorphism group of finite geometry. For example, a glance through the
list of distance regular graphs of diameter d < 3 reveals the fact that most of them are connected with
classical Lie geometry. Theory of distance regular graphs is an important part of algebraic combinatorics
and its applications such as coding theory, communication networks, and block design. An important tool
for investigation of such graphs is their spectra, which is the set of eigenvalues of adjacency matrix of a
graph. Let G be a finite simple group of Lie type and X be the set homogeneous elements of the associated
geometry.
Graph Theory
This document provides an overview of graph theory, including its history and key concepts. It defines a graph as a set of nodes connected by edges. Important early contributors are noted, such as Euler's work on the Seven Bridges of Königsberg problem. Exact definitions of graph types like simple, directed, and weighted graphs are given. Key graph concepts explained in less than three sentences include connectivity, degree, paths, cycles, and trees. The document also briefly discusses representing graphs through adjacency lists and matrices. It concludes by thanking the reader and listing references.
An Overview Applications Of Graph Theory In Real Field
This document provides an overview of applications of graph theory in various real-world fields. It begins by defining some key graph theory concepts such as vertices, edges, directed and undirected graphs, connected graphs, and adjacency matrices. It then discusses how graph theory originated from the Königsberg bridge problem in 1735. The document outlines how graph theory has gradually expanded and is now used widely in fields like computer science, biochemistry, genomics, electrical engineering, and operations research. It identifies some common applications of graph theory such as resource allocation, network formation, data mining, and image processing. In conclusion, the document aims to provide basic graph theory concepts and demonstrate how they are applied across different domains.
Introduction to Graph Theory
The document provides an introduction to graph theory. It begins with a brief history, noting that graph theory originated from Euler's work on the Konigsberg bridges problem in 1735. It then discusses basic concepts such as graphs being collections of nodes and edges, different types of graphs like directed and undirected graphs. Key graph theory terms are defined such as vertices, edges, degree, walks, paths, cycles and shortest paths. Different graph representations like the adjacency matrix, incidence matrix and adjacency lists are also introduced. The document is intended to provide an overview of fundamental graph theory concepts.
Introduction to Graphs
Introduction to Graphs
Topics:
Definition: Graph
Related Definitions
Applications
Teaching material for the course of "Tecniche di Programmazione" at Politecnico di Torino in year 2012/2013. More information: http://bit.ly/tecn-progr
Semantic Data Management in Graph Databases
The tutorial describes existing approaches to model graph databases and different techniques implemented in RDF and Database engines including their main drawbacks when a large volume of interconnected data needs to be traversed.
Discrete-Chapter 11 Graphs Part I
1. The document defines different types of graphs including simple graphs, multigraphs, pseudographs, directed graphs, and mixed graphs. It provides examples of how graphs can model real-world networks and relationships.
2. Graph terminology is defined, including adjacent vertices, neighborhoods, degrees of vertices, and the handshaking theorem. Examples show how to calculate degrees and neighborhoods in sample graphs.
3. Directed graphs are discussed, defining in-degrees and out-degrees of vertices in directed graphs.
Graph Theory
The document provides information about a faculty development program on discrete mathematics. It includes:
- An outline of the course content which covers topics like graph terminology, representations of graphs, connectivity, Euler and Hamilton paths, shortest path algorithms, planar graphs, and graph coloring.
- Details of learning resources including textbooks and reference books.
- A table listing the topics to be discussed in lectures, related self-learning tasks, reference materials and number of contact hours.
- Information on representation of graphs through adjacency matrix, incidence matrix and adjacency lists.
The program aims to teach key concepts in graph theory and related algorithms over 6 lecture hours through lectures, self-study and reference books.
Graphs
The document discusses graphs and their representations. It defines a graph as consisting of nodes and edges that connect pairs of nodes. Adjacency lists are described as the standard way to represent graphs, with each edge appearing in two lists doubling the space. A topological sort orders the vertices in a directed acyclic graph such that if there is an edge from vertex v to w, then v appears before w in the ordering. It is not possible to perform a topological sort if the graph contains a cycle. Pseudocode for a topological sort algorithm is provided.
Slides Chapter10.1 10.2
This section provides an introduction to graphs and graph theory. Key points include:
- Graphs consist of vertices and edges that connect the vertices. They can be directed or undirected.
- Common terminology is introduced, such as adjacent vertices, neighborhoods, degrees of vertices, and handshaking theorem.
- Different types of graphs are discussed, including multigraphs, pseudographs, and directed graphs.
- Examples of graph models are given for computer networks, social networks, information networks, transportation networks, and software design. Graphs can be used to model many real-world systems and applications.
Graph terminology and algorithm and tree.pptx
This document provides an overview of key concepts in graph theory including graph terminology, representations, traversals, spanning trees, minimum spanning trees, and shortest path algorithms. It defines graphs, directed vs undirected graphs, connectedness, degrees, adjacency, paths, cycles, trees, and graph representations using adjacency matrices and lists. It also describes breadth-first and depth-first traversals, spanning trees, minimum spanning trees, and algorithms for finding minimum spanning trees and shortest paths like Kruskal's, Prim's, Dijkstra's, Bellman-Ford and A* algorithms.
A glimpse to topological graph theory
This document provides an introduction to topological graph theory. It begins with definitions of basic graph theory concepts from a topological perspective, such as representing graphs with curved arcs instead of straight lines. It then discusses graph drawings, incidence matrices, vertex valence, and graph maps/isomorphisms. Important classes of graphs are introduced, such as trees, paths, cycles, and complete graphs. The document aims to introduce preliminary concepts of topological graph theory to students in a simple manner.
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
More Related Content
Similar to Poster on Graph Terminologies
Graph theory concepts complex networks presents-rouhollah nabati
This document provides an introduction to network and social network analysis theory, including basic concepts of graph theory and network structures. It defines what a network and graph are, explains what network theory techniques are used for, and gives examples of real-world networks that can be represented as graphs. It also summarizes key graph theory concepts such as nodes, edges, walks, paths, cycles, connectedness, degree, and centrality measures.
Graph
Dr. M. Usha of the Department of Computer Science and Engineering at Velammal Engineering College gave a presentation on graphs. A graph is a non-linear data structure consisting of nodes and edges. Graphs can be used to represent networks and are applied in areas like social networks. Common graph terminology includes vertices, edges, paths, cycles, and graph representations like adjacency matrices and adjacency lists. Graph traversal algorithms like depth-first search and breadth-first search are used to systematically visit all nodes.
FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATION
Data mining algorithms are facing the challenge to deal with an increasing number of complex
objects. Graph is a natural data structure used for modeling complex objects. Frequent subgraph
mining is another active research topic in data mining . A graph is a general model to represent
data and has been used in many domains like cheminformatics and bioinformatics. Mining
patterns from graph databases is challenging since graph related operations, such as subgraph
testing, generally have higher time complexity than the corresponding operations on itemsets,
sequences, and trees. Many frequent subgraph Mining algorithms have been proposed. SPIN,
SUBDUE, g_Span, FFSM, GREW are a few to mention. In this paper we present a detailed
survey on frequent subgraph mining algorithms, which are used for knowledge discovery in
complex objects and also propose a frame work for classification of these algorithms. The
purpose is to help user to apply the techniques in a task specific manner in various application domains and to pave wave for further research.
Data Structures and Agorithm: DS 21 Graph Theory.pptx
Data Structures and Agorithm: DS 21 Graph Theory.pptx
Tn 110 lecture 8
Graph theory has many applications in modeling real-world networks like computer networks and chemical compounds. It studies graphs, which are collections of vertices connected by edges. A graph can be represented using an incidence matrix or adjacency list/matrix. Connectivity refers to reachability between vertices by traversing edges. The shortest path problem finds the minimum weight path between vertices in a weighted graph. Planar graphs can be drawn in a plane without edge crossings, and satisfy Euler's formula relating vertices, edges, and regions. Graph coloring assigns different colors to connected elements like vertices or edges.
On algorithmic problems concerning graphs of higher degree of symmetry
Since the ancient determination of the five platonic solids the study of symmetry and regularity has always
been one of the most fascinating aspects of mathematics. One intriguing phenomenon of studies in graph
theory is the fact that quite often arithmetic regularity properties of a graph imply the existence of many
symmetries, i.e. large automorphism group G. In some important special situation higher degree of
regularity means that G is an automorphism group of finite geometry. For example, a glance through the
list of distance regular graphs of diameter d < 3 reveals the fact that most of them are connected with
classical Lie geometry. Theory of distance regular graphs is an important part of algebraic combinatorics
and its applications such as coding theory, communication networks, and block design. An important tool
for investigation of such graphs is their spectra, which is the set of eigenvalues of adjacency matrix of a
graph. Let G be a finite simple group of Lie type and X be the set homogeneous elements of the associated
geometry. The complexity of computing the adjacency matrices of a graph Gr on the vertices X such that
Aut GR = G depends very much on the description of the geometry with which one starts. For example, we
can represent the geometry as the totality of 1 cosets of parabolic subgroups 2 chains of embedded
subspaces (case of linear groups), or totally isotropic subspaces (case of the remaining classical groups), 3
special subspaces of minimal module for G which are defined in terms of a G invariant multilinear form.
The aim of this research is to develop an effective method for generation of graphs connected with classical
geometry and evaluation of its spectra, which is the set of eigenvalues of adjacency matrix of a graph. The
main approach is to avoid manual drawing and to calculate graph layout automatically according to its
formal structure. This is a simple task in a case of a tree like graph with a strict hierarchy of entities but it
becomes more complicated for graphs of geometrical nature. There are two main reasons for the
investigations of spectra: (1) very often spectra carry much more useful information about the graph than a
corresponding list of entities and relationships (2) graphs with special spectra, satisfying so called
Ramanujan property or simply Ramanujan graphs (by name of Indian genius mathematician) are important
for real life applications (see [13]). There is a motivated suspicion that among geometrical graphs one
could find some new Ramanujan graphs.
Graph theory and its applications
This document is a project report submitted by S. Manikanta in partial fulfillment of the requirements for a Master of Science degree in Mathematics. The report discusses applications of graph theory. It provides an overview of graph theory concepts such as definitions of graphs, terminology used in graph theory, different types of graphs, trees and forests, graph isomorphism and operations, walks and paths in graphs, representations of graphs using matrices, applications of graphs in areas like computer science, fingerprint recognition, security, and more. The document also includes examples and illustrations to explain various graph theory concepts.
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
Since the ancient determination of the five platonic solids the study of symmetry and regularity has always
been one of the most fascinating aspects of mathematics. One intriguing phenomenon of studies in graph
theory is the fact that quite often arithmetic regularity properties of a graph imply the existence of many
symmetries, i.e. large automorphism group G. In some important special situation higher degree of
regularity means that G is an automorphism group of finite geometry. For example, a glance through the
list of distance regular graphs of diameter d < 3 reveals the fact that most of them are connected with
classical Lie geometry. Theory of distance regular graphs is an important part of algebraic combinatorics
and its applications such as coding theory, communication networks, and block design. An important tool
for investigation of such graphs is their spectra, which is the set of eigenvalues of adjacency matrix of a
graph. Let G be a finite simple group of Lie type and X be the set homogeneous elements of the associated
geometry.
Graph Theory
This document provides an overview of graph theory, including its history and key concepts. It defines a graph as a set of nodes connected by edges. Important early contributors are noted, such as Euler's work on the Seven Bridges of Königsberg problem. Exact definitions of graph types like simple, directed, and weighted graphs are given. Key graph concepts explained in less than three sentences include connectivity, degree, paths, cycles, and trees. The document also briefly discusses representing graphs through adjacency lists and matrices. It concludes by thanking the reader and listing references.
An Overview Applications Of Graph Theory In Real Field
This document provides an overview of applications of graph theory in various real-world fields. It begins by defining some key graph theory concepts such as vertices, edges, directed and undirected graphs, connected graphs, and adjacency matrices. It then discusses how graph theory originated from the Königsberg bridge problem in 1735. The document outlines how graph theory has gradually expanded and is now used widely in fields like computer science, biochemistry, genomics, electrical engineering, and operations research. It identifies some common applications of graph theory such as resource allocation, network formation, data mining, and image processing. In conclusion, the document aims to provide basic graph theory concepts and demonstrate how they are applied across different domains.
Introduction to Graph Theory
The document provides an introduction to graph theory. It begins with a brief history, noting that graph theory originated from Euler's work on the Konigsberg bridges problem in 1735. It then discusses basic concepts such as graphs being collections of nodes and edges, different types of graphs like directed and undirected graphs. Key graph theory terms are defined such as vertices, edges, degree, walks, paths, cycles and shortest paths. Different graph representations like the adjacency matrix, incidence matrix and adjacency lists are also introduced. The document is intended to provide an overview of fundamental graph theory concepts.
Introduction to Graphs
Introduction to Graphs
Topics:
Definition: Graph
Related Definitions
Applications
Teaching material for the course of "Tecniche di Programmazione" at Politecnico di Torino in year 2012/2013. More information: http://bit.ly/tecn-progr
Semantic Data Management in Graph Databases
The tutorial describes existing approaches to model graph databases and different techniques implemented in RDF and Database engines including their main drawbacks when a large volume of interconnected data needs to be traversed.
Discrete-Chapter 11 Graphs Part I
1. The document defines different types of graphs including simple graphs, multigraphs, pseudographs, directed graphs, and mixed graphs. It provides examples of how graphs can model real-world networks and relationships.
2. Graph terminology is defined, including adjacent vertices, neighborhoods, degrees of vertices, and the handshaking theorem. Examples show how to calculate degrees and neighborhoods in sample graphs.
3. Directed graphs are discussed, defining in-degrees and out-degrees of vertices in directed graphs.
Graph Theory
The document provides information about a faculty development program on discrete mathematics. It includes:
- An outline of the course content which covers topics like graph terminology, representations of graphs, connectivity, Euler and Hamilton paths, shortest path algorithms, planar graphs, and graph coloring.
- Details of learning resources including textbooks and reference books.
- A table listing the topics to be discussed in lectures, related self-learning tasks, reference materials and number of contact hours.
- Information on representation of graphs through adjacency matrix, incidence matrix and adjacency lists.
The program aims to teach key concepts in graph theory and related algorithms over 6 lecture hours through lectures, self-study and reference books.
Graphs
The document discusses graphs and their representations. It defines a graph as consisting of nodes and edges that connect pairs of nodes. Adjacency lists are described as the standard way to represent graphs, with each edge appearing in two lists doubling the space. A topological sort orders the vertices in a directed acyclic graph such that if there is an edge from vertex v to w, then v appears before w in the ordering. It is not possible to perform a topological sort if the graph contains a cycle. Pseudocode for a topological sort algorithm is provided.
Slides Chapter10.1 10.2
This section provides an introduction to graphs and graph theory. Key points include:
- Graphs consist of vertices and edges that connect the vertices. They can be directed or undirected.
- Common terminology is introduced, such as adjacent vertices, neighborhoods, degrees of vertices, and handshaking theorem.
- Different types of graphs are discussed, including multigraphs, pseudographs, and directed graphs.
- Examples of graph models are given for computer networks, social networks, information networks, transportation networks, and software design. Graphs can be used to model many real-world systems and applications.
Graph terminology and algorithm and tree.pptx
This document provides an overview of key concepts in graph theory including graph terminology, representations, traversals, spanning trees, minimum spanning trees, and shortest path algorithms. It defines graphs, directed vs undirected graphs, connectedness, degrees, adjacency, paths, cycles, trees, and graph representations using adjacency matrices and lists. It also describes breadth-first and depth-first traversals, spanning trees, minimum spanning trees, and algorithms for finding minimum spanning trees and shortest paths like Kruskal's, Prim's, Dijkstra's, Bellman-Ford and A* algorithms.
A glimpse to topological graph theory
This document provides an introduction to topological graph theory. It begins with definitions of basic graph theory concepts from a topological perspective, such as representing graphs with curved arcs instead of straight lines. It then discusses graph drawings, incidence matrices, vertex valence, and graph maps/isomorphisms. Important classes of graphs are introduced, such as trees, paths, cycles, and complete graphs. The document aims to introduce preliminary concepts of topological graph theory to students in a simple manner.
Similar to Poster on Graph Terminologies (20)
Graph theory concepts complex networks presents-rouhollah nabati
Graph theory concepts complex networks presents-rouhollah nabati
FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATION
FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATION
Data Structures and Agorithm: DS 21 Graph Theory.pptx
Data Structures and Agorithm: DS 21 Graph Theory.pptx
On algorithmic problems concerning graphs of higher degree of symmetry
On algorithmic problems concerning graphs of higher degree of symmetry
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
An Overview Applications Of Graph Theory In Real Field
An Overview Applications Of Graph Theory In Real Field
Recently uploaded
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
bank management system in java and mysql report1.pdf
truth is high but higher still is truth full living
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
Kinematics 11th jpp- 01. ( Solved ) unacademy namo kaul on 14th may...
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Modelagem de um CSTR com reação endotermica.pdf
Modelagem em função de transferencia. CSTR não-linear.
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Recycled Concrete Aggregate in Construction Part III
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
A review on techniques and modelling methodologies used for checking electrom...
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Understanding Inductive Bias in Machine Learning
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Recently uploaded (20)
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
bank management system in java and mysql report1.pdf
bank management system in java and mysql report1.pdf
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
Recycled Concrete Aggregate in Construction Part III
Recycled Concrete Aggregate in Construction Part III
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
2. Operations Strategy in a Global Environment.ppt
2. Operations Strategy in a Global Environment.ppt
A review on techniques and modelling methodologies used for checking electrom...
A review on techniques and modelling methodologies used for checking electrom...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
Manufacturing Process of molasses based distillery ppt.pptx
Manufacturing Process of molasses based distillery ppt.pptx
Poster on Graph Terminologies
- 1. G. H. RAISONI COLLEGE OF ENGINEERING (An Autonomous Institute affiliated by R. T. M. Nagpur University under UGC Act 1956) Department of Information Technology SESSION 2016-2017 NAME: DEEPAK R TATHE ROLL NO: 37 TOPIC: GRAPH TERMINOLOGIES SUB: DATA STRUCTURE What is a Graph? A graphG=(V,E) is composedof V : Set of vertices E: Set of edges connectingthe vertices V V={2,3,4,5,6,7} E={ (2,4),(4,5),(5,7),( 3,7)} EDGE VERTICES Edges, also called as arcs, are represented by (u,v) and are either directed or undirected. Kinds of Graph: Directed and Undirected GRAPH TERMINOLOGIES Adjacent nodes: Two nodes are adjacent if they are connected by an edge Path: A sequence of vertices that connect two nodes In a graph A simple path is a path in which all vertices,except Possibly in the first and last, are different. Complete graph: A graph in which every vertex is directly connected to every other vertex. 5 7 Connected Graph: Any two vertices are connected by some path. Connected Not connected Subgraph: Subset of vertices and edges forming a graph