SlideShare a Scribd company logo
1 of 21
 The word "connectivity" may refer to the
several meanings in day to day life.
Generally, it may refer to the connection
between two or more things or virtues
 A connected graph is an undirected graph
that has a path between every pair of
vertices
 A connected graph with at least 3 vertices is
1-connected if the removal of 1 vertex
disconnects the graph
 A bi-connected graph is a connected graph in
which there exist two vertices for which
there are two disjoint paths between these
two vertices.
 The connected graph is said to be
a undirected graph which has at least one
path between each pair of vertices.
 A graph is connected when there is a path
between every pair of vertices. In a
connected graph, there are
no unreachable vertices. A graph that is not
connected is disconnected.
 A graph G is said to be disconnected if there
exist two nodes in G such that no path in G
has those nodes as endpoints.

A graph with just one vertex is connected.
An edgeless graph with two or more vertices
is disconnected.
 In the area of information and technology,
the connectivity may refer to internet
connectivity by which various individual
computers, cell phones and LANs can be
connected to global Internet.
 In mathematics and computer
science, connectivity is one of the basic
concepts of graph theory: It is closely related
to the theory of network flow problems. The
connectivity of a graph is an important
measure of its resilience as a network.
An undirected graph is connected if
there is a path between every pair of
distinct vertices in the graph.
Connected component:
 maximal connected subgraph. (An
unconnected graph will have several
component)
 Component of graph or subgraphs:
a
b
c
d e
f g
h
A cut vertex separates one connected
component into several components if it is
removed.
OR
Cut vertices are vertices that produce a
subgraph with more connected components
when removed from a graph (and all incident
edges to it). Removing a cut vertex v in in a
connected graph G will make G disconnected.
A cut edge separates one connected
component into two components if it is
removed
OR
Cut edges or bridges are edges that
produce a subgraph with more connected
components when removed from a graph.
Removing a cut edge (u; v) in a connected
graph G will make G disconnected
.Find the cut vertices and cut edges in the
graph G.
Sol:
cut edges: cut vertices:
{a, b}, {c, e} b, c, e
b
a
c
d
e h
gf
G
 An directed graph is connected if there is
a path is directional between every pair
of distinct vertices in the graph.
 Def. 4: A directed graph is strongly connected if
there is a path from a to b for any two vertices a,
b.
 A directed graph is weakly connected if there is
a path between every two vertices in the
underlying undirected graphs.
 Definition:
 , G1 and G2 are isomorphic if their vertices can
be ordered in such a way that the adjacency
matrices MG1
and MG2
are identical.
 Function of isomorphic graph like f(G) is called
an isomorphism.
 Properties that two isomorphic simple graphs
must both have they must have :
• the same number of vertices,
• the same number of edges, and
• the same degrees of vertices.
Def.
G=(V, E) : simple graph, V={v1,v2,…,vn}. (Order doesn't matter)
A matrix A is called the adjacency matrix of G
if A=[aij]nn , where aij = 1, if {vi,vj}E,
0, otherwise.
Connectivity in Graphs: Types and Concepts

More Related Content

What's hot

Cs6702 graph theory and applications 2 marks questions and answers
Cs6702 graph theory and applications 2 marks questions and answersCs6702 graph theory and applications 2 marks questions and answers
Cs6702 graph theory and applications 2 marks questions and answersappasami
 
Graphs in Data Structure
 Graphs in Data Structure Graphs in Data Structure
Graphs in Data Structurehafsa komal
 
Interesting applications of graph theory
Interesting applications of graph theoryInteresting applications of graph theory
Interesting applications of graph theoryTech_MX
 
Graphs In Data Structure
Graphs In Data StructureGraphs In Data Structure
Graphs In Data StructureAnuj Modi
 
Graph isomorphism
Graph isomorphismGraph isomorphism
Graph isomorphismCore Condor
 
Introduction to graph theory (All chapter)
Introduction to graph theory (All chapter)Introduction to graph theory (All chapter)
Introduction to graph theory (All chapter)sobia1122
 
ADA - Minimum Spanning Tree Prim Kruskal and Dijkstra
ADA - Minimum Spanning Tree Prim Kruskal and Dijkstra ADA - Minimum Spanning Tree Prim Kruskal and Dijkstra
ADA - Minimum Spanning Tree Prim Kruskal and Dijkstra Sahil Kumar
 
Graphs in datastructures
Graphs in datastructuresGraphs in datastructures
Graphs in datastructuresLikhithaGunturi
 
Dijkstra’s algorithm
Dijkstra’s algorithmDijkstra’s algorithm
Dijkstra’s algorithmfaisal2204
 
Cs6702 graph theory and applications Anna University question paper apr may 2...
Cs6702 graph theory and applications Anna University question paper apr may 2...Cs6702 graph theory and applications Anna University question paper apr may 2...
Cs6702 graph theory and applications Anna University question paper apr may 2...appasami
 

What's hot (20)

Graph theory
Graph theoryGraph theory
Graph theory
 
Graph theory
Graph theoryGraph theory
Graph theory
 
Graph theory
Graph theoryGraph theory
Graph theory
 
Cs6702 graph theory and applications 2 marks questions and answers
Cs6702 graph theory and applications 2 marks questions and answersCs6702 graph theory and applications 2 marks questions and answers
Cs6702 graph theory and applications 2 marks questions and answers
 
Graphs in Data Structure
 Graphs in Data Structure Graphs in Data Structure
Graphs in Data Structure
 
Minimum spanning tree
Minimum spanning treeMinimum spanning tree
Minimum spanning tree
 
Spanning trees
Spanning treesSpanning trees
Spanning trees
 
graph theory
graph theory graph theory
graph theory
 
Interesting applications of graph theory
Interesting applications of graph theoryInteresting applications of graph theory
Interesting applications of graph theory
 
Graphs In Data Structure
Graphs In Data StructureGraphs In Data Structure
Graphs In Data Structure
 
Graph data structure and algorithms
Graph data structure and algorithmsGraph data structure and algorithms
Graph data structure and algorithms
 
Graph isomorphism
Graph isomorphismGraph isomorphism
Graph isomorphism
 
Graph Theory
Graph TheoryGraph Theory
Graph Theory
 
Graph theory
Graph  theoryGraph  theory
Graph theory
 
Introduction to graph theory (All chapter)
Introduction to graph theory (All chapter)Introduction to graph theory (All chapter)
Introduction to graph theory (All chapter)
 
Introduction to Graph Theory
Introduction to Graph TheoryIntroduction to Graph Theory
Introduction to Graph Theory
 
ADA - Minimum Spanning Tree Prim Kruskal and Dijkstra
ADA - Minimum Spanning Tree Prim Kruskal and Dijkstra ADA - Minimum Spanning Tree Prim Kruskal and Dijkstra
ADA - Minimum Spanning Tree Prim Kruskal and Dijkstra
 
Graphs in datastructures
Graphs in datastructuresGraphs in datastructures
Graphs in datastructures
 
Dijkstra’s algorithm
Dijkstra’s algorithmDijkstra’s algorithm
Dijkstra’s algorithm
 
Cs6702 graph theory and applications Anna University question paper apr may 2...
Cs6702 graph theory and applications Anna University question paper apr may 2...Cs6702 graph theory and applications Anna University question paper apr may 2...
Cs6702 graph theory and applications Anna University question paper apr may 2...
 

Similar to Connectivity in Graphs: Types and Concepts

Map Coloring and Some of Its Applications
Map Coloring and Some of Its Applications Map Coloring and Some of Its Applications
Map Coloring and Some of Its Applications MD SHAH ALAM
 
Slides Chapter10.1 10.2
Slides Chapter10.1 10.2Slides Chapter10.1 10.2
Slides Chapter10.1 10.2showslidedump
 
FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATION
FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATIONFREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATION
FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATIONcscpconf
 
Graph theory concepts complex networks presents-rouhollah nabati
Graph theory concepts   complex networks presents-rouhollah nabatiGraph theory concepts   complex networks presents-rouhollah nabati
Graph theory concepts complex networks presents-rouhollah nabatinabati
 
NON-LINEAR DATA STRUCTURE-Graphs.pptx
NON-LINEAR DATA STRUCTURE-Graphs.pptxNON-LINEAR DATA STRUCTURE-Graphs.pptx
NON-LINEAR DATA STRUCTURE-Graphs.pptxRajitha Reddy Alugati
 
VANU no sql ppt.pptx
VANU no sql ppt.pptxVANU no sql ppt.pptx
VANU no sql ppt.pptxMJeyavarthini
 
Graph terminology and algorithm and tree.pptx
Graph terminology and algorithm and tree.pptxGraph terminology and algorithm and tree.pptx
Graph terminology and algorithm and tree.pptxasimshahzad8611
 
Lecture 5b graphs and hashing
Lecture 5b graphs and hashingLecture 5b graphs and hashing
Lecture 5b graphs and hashingVictor Palmar
 
Connected and disconnected graph
Connected and disconnected graphConnected and disconnected graph
Connected and disconnected graphABDULLAHSHAUKAT4
 

Similar to Connectivity in Graphs: Types and Concepts (20)

Map Coloring and Some of Its Applications
Map Coloring and Some of Its Applications Map Coloring and Some of Its Applications
Map Coloring and Some of Its Applications
 
Spanningtreesppt
SpanningtreespptSpanningtreesppt
Spanningtreesppt
 
FCS (graphs).pptx
FCS (graphs).pptxFCS (graphs).pptx
FCS (graphs).pptx
 
Slides Chapter10.1 10.2
Slides Chapter10.1 10.2Slides Chapter10.1 10.2
Slides Chapter10.1 10.2
 
FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATION
FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATIONFREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATION
FREQUENT SUBGRAPH MINING ALGORITHMS - A SURVEY AND FRAMEWORK FOR CLASSIFICATION
 
Strongly connected components
Strongly connected componentsStrongly connected components
Strongly connected components
 
Graph.pptx
Graph.pptxGraph.pptx
Graph.pptx
 
Graph theory concepts complex networks presents-rouhollah nabati
Graph theory concepts   complex networks presents-rouhollah nabatiGraph theory concepts   complex networks presents-rouhollah nabati
Graph theory concepts complex networks presents-rouhollah nabati
 
Siegel
SiegelSiegel
Siegel
 
NON-LINEAR DATA STRUCTURE-Graphs.pptx
NON-LINEAR DATA STRUCTURE-Graphs.pptxNON-LINEAR DATA STRUCTURE-Graphs.pptx
NON-LINEAR DATA STRUCTURE-Graphs.pptx
 
Graphs.pptx
Graphs.pptxGraphs.pptx
Graphs.pptx
 
graphs
graphsgraphs
graphs
 
Vanmathy no sql
Vanmathy no sql Vanmathy no sql
Vanmathy no sql
 
VANU no sql ppt.pptx
VANU no sql ppt.pptxVANU no sql ppt.pptx
VANU no sql ppt.pptx
 
Isograph
IsographIsograph
Isograph
 
Graph terminology and algorithm and tree.pptx
Graph terminology and algorithm and tree.pptxGraph terminology and algorithm and tree.pptx
Graph terminology and algorithm and tree.pptx
 
Electrical Network Topology
Electrical Network TopologyElectrical Network Topology
Electrical Network Topology
 
Lecture 5b graphs and hashing
Lecture 5b graphs and hashingLecture 5b graphs and hashing
Lecture 5b graphs and hashing
 
Connected and disconnected graph
Connected and disconnected graphConnected and disconnected graph
Connected and disconnected graph
 
logic.pptx
logic.pptxlogic.pptx
logic.pptx
 

More from sana younas

7 habits of highly effective people
7 habits of highly effective people7 habits of highly effective people
7 habits of highly effective peoplesana younas
 
Shortest path algorithm
Shortest path algorithmShortest path algorithm
Shortest path algorithmsana younas
 
circular linklist
circular linklistcircular linklist
circular linklistsana younas
 
Enterpise system
Enterpise systemEnterpise system
Enterpise systemsana younas
 
Database administration
Database administrationDatabase administration
Database administrationsana younas
 
Universal logic gate
Universal logic gateUniversal logic gate
Universal logic gatesana younas
 
Object oriented programming
Object oriented programmingObject oriented programming
Object oriented programmingsana younas
 
Memory management
Memory managementMemory management
Memory managementsana younas
 

More from sana younas (16)

7 habits of highly effective people
7 habits of highly effective people7 habits of highly effective people
7 habits of highly effective people
 
Shortest path algorithm
Shortest path algorithmShortest path algorithm
Shortest path algorithm
 
Binary search
Binary searchBinary search
Binary search
 
circular linklist
circular linklistcircular linklist
circular linklist
 
Link list 2
Link list 2Link list 2
Link list 2
 
Link list 1
Link list 1Link list 1
Link list 1
 
Heapsort 1
Heapsort 1Heapsort 1
Heapsort 1
 
Arrays
ArraysArrays
Arrays
 
Enterpise system
Enterpise systemEnterpise system
Enterpise system
 
Database administration
Database administrationDatabase administration
Database administration
 
Encoders
EncodersEncoders
Encoders
 
Universal logic gate
Universal logic gateUniversal logic gate
Universal logic gate
 
Object oriented programming
Object oriented programmingObject oriented programming
Object oriented programming
 
Polymorphism
PolymorphismPolymorphism
Polymorphism
 
Memory management
Memory managementMemory management
Memory management
 
Parallel adders
Parallel addersParallel adders
Parallel adders
 

Recently uploaded

ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4MiaBumagat1
 
Judging the Relevance and worth of ideas part 2.pptx
Judging the Relevance  and worth of ideas part 2.pptxJudging the Relevance  and worth of ideas part 2.pptx
Judging the Relevance and worth of ideas part 2.pptxSherlyMaeNeri
 
Science 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptxScience 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptxMaryGraceBautista27
 
Global Lehigh Strategic Initiatives (without descriptions)
Global Lehigh Strategic Initiatives (without descriptions)Global Lehigh Strategic Initiatives (without descriptions)
Global Lehigh Strategic Initiatives (without descriptions)cama23
 
Earth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatEarth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatYousafMalik24
 
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...Postal Advocate Inc.
 
Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17Celine George
 
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxINTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxHumphrey A Beña
 
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATIONTHEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATIONHumphrey A Beña
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Celine George
 
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxMULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxAnupkumar Sharma
 
How to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPHow to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPCeline George
 
How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17Celine George
 
Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Celine George
 
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfVirtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfErwinPantujan2
 
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...Nguyen Thanh Tu Collection
 
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTSGRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTSJoshuaGantuangco2
 

Recently uploaded (20)

ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4ANG SEKTOR NG agrikultura.pptx QUARTER 4
ANG SEKTOR NG agrikultura.pptx QUARTER 4
 
Judging the Relevance and worth of ideas part 2.pptx
Judging the Relevance  and worth of ideas part 2.pptxJudging the Relevance  and worth of ideas part 2.pptx
Judging the Relevance and worth of ideas part 2.pptx
 
Science 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptxScience 7 Quarter 4 Module 2: Natural Resources.pptx
Science 7 Quarter 4 Module 2: Natural Resources.pptx
 
Global Lehigh Strategic Initiatives (without descriptions)
Global Lehigh Strategic Initiatives (without descriptions)Global Lehigh Strategic Initiatives (without descriptions)
Global Lehigh Strategic Initiatives (without descriptions)
 
Earth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatEarth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice great
 
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
USPS® Forced Meter Migration - How to Know if Your Postage Meter Will Soon be...
 
Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17Field Attribute Index Feature in Odoo 17
Field Attribute Index Feature in Odoo 17
 
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptxINTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
INTRODUCTION TO CATHOLIC CHRISTOLOGY.pptx
 
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATIONTHEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
THEORIES OF ORGANIZATION-PUBLIC ADMINISTRATION
 
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
Incoming and Outgoing Shipments in 3 STEPS Using Odoo 17
 
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptxMULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
MULTIDISCIPLINRY NATURE OF THE ENVIRONMENTAL STUDIES.pptx
 
How to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERPHow to do quick user assign in kanban in Odoo 17 ERP
How to do quick user assign in kanban in Odoo 17 ERP
 
Raw materials used in Herbal Cosmetics.pptx
Raw materials used in Herbal Cosmetics.pptxRaw materials used in Herbal Cosmetics.pptx
Raw materials used in Herbal Cosmetics.pptx
 
How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17How to Add Barcode on PDF Report in Odoo 17
How to Add Barcode on PDF Report in Odoo 17
 
Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17Difference Between Search & Browse Methods in Odoo 17
Difference Between Search & Browse Methods in Odoo 17
 
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptxLEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
LEFT_ON_C'N_ PRELIMS_EL_DORADO_2024.pptx
 
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdfVirtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
Virtual-Orientation-on-the-Administration-of-NATG12-NATG6-and-ELLNA.pdf
 
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
HỌC TỐT TIẾNG ANH 11 THEO CHƯƠNG TRÌNH GLOBAL SUCCESS ĐÁP ÁN CHI TIẾT - CẢ NĂ...
 
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptxFINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
FINALS_OF_LEFT_ON_C'N_EL_DORADO_2024.pptx
 
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTSGRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
GRADE 4 - SUMMATIVE TEST QUARTER 4 ALL SUBJECTS
 

Connectivity in Graphs: Types and Concepts

  • 1.  The word "connectivity" may refer to the several meanings in day to day life. Generally, it may refer to the connection between two or more things or virtues
  • 2.  A connected graph is an undirected graph that has a path between every pair of vertices
  • 3.  A connected graph with at least 3 vertices is 1-connected if the removal of 1 vertex disconnects the graph
  • 4.
  • 5.  A bi-connected graph is a connected graph in which there exist two vertices for which there are two disjoint paths between these two vertices.
  • 6.  The connected graph is said to be a undirected graph which has at least one path between each pair of vertices.
  • 7.  A graph is connected when there is a path between every pair of vertices. In a connected graph, there are no unreachable vertices. A graph that is not connected is disconnected.  A graph G is said to be disconnected if there exist two nodes in G such that no path in G has those nodes as endpoints.  A graph with just one vertex is connected. An edgeless graph with two or more vertices is disconnected.
  • 8.  In the area of information and technology, the connectivity may refer to internet connectivity by which various individual computers, cell phones and LANs can be connected to global Internet.
  • 9.  In mathematics and computer science, connectivity is one of the basic concepts of graph theory: It is closely related to the theory of network flow problems. The connectivity of a graph is an important measure of its resilience as a network.
  • 10. An undirected graph is connected if there is a path between every pair of distinct vertices in the graph. Connected component:  maximal connected subgraph. (An unconnected graph will have several component)
  • 11.  Component of graph or subgraphs: a b c d e f g h
  • 12. A cut vertex separates one connected component into several components if it is removed. OR Cut vertices are vertices that produce a subgraph with more connected components when removed from a graph (and all incident edges to it). Removing a cut vertex v in in a connected graph G will make G disconnected.
  • 13. A cut edge separates one connected component into two components if it is removed OR Cut edges or bridges are edges that produce a subgraph with more connected components when removed from a graph. Removing a cut edge (u; v) in a connected graph G will make G disconnected
  • 14. .Find the cut vertices and cut edges in the graph G. Sol: cut edges: cut vertices: {a, b}, {c, e} b, c, e b a c d e h gf G
  • 15.  An directed graph is connected if there is a path is directional between every pair of distinct vertices in the graph.
  • 16.  Def. 4: A directed graph is strongly connected if there is a path from a to b for any two vertices a, b.
  • 17.  A directed graph is weakly connected if there is a path between every two vertices in the underlying undirected graphs.
  • 18.  Definition:  , G1 and G2 are isomorphic if their vertices can be ordered in such a way that the adjacency matrices MG1 and MG2 are identical.  Function of isomorphic graph like f(G) is called an isomorphism.
  • 19.  Properties that two isomorphic simple graphs must both have they must have : • the same number of vertices, • the same number of edges, and • the same degrees of vertices.
  • 20. Def. G=(V, E) : simple graph, V={v1,v2,…,vn}. (Order doesn't matter) A matrix A is called the adjacency matrix of G if A=[aij]nn , where aij = 1, if {vi,vj}E, 0, otherwise.

Editor's Notes

  1. Note: There are n! different adjacency matrices for a graph with n vertices. The adjacency matrix of an undirected graph is symmetric. aii = 0 (simple matrix has no loop.