SlideShare a Scribd company logo
1 of 12
Download to read offline
Social Network Analysis Study
        Chapter 3 - 4.2




      Jan 8 2013, 박성규
(1) Chapter 3: Notation for social Network Data

 3가지 Notations
   Graph theoretic: centrality and prestige methods, cohesive
    subgroup ideas, dyadic and triadic methods
       Actor 와 Relation 으로 표현되는 scheme


   Sociometric: study of structural equivalence and blockmodels
       Social network 분야에서 가장 일반적인 notation


   Algebraic: role and positional analyses and relational algebras
       Multiple relation 에서 많이 사용됨
(1) Chapter 3: Notation for social Network Data

   Graph theoretic notation
  Set of actors: N={n1, n2, ng}
  Set of relations (lines, ordered pairs of actors, arcs, ties): L={l1,l2,lL}
  Graph는 수학적으로 두 개의 Set 으로 정리 가능: G={N, L}
  Self choices 는 일반적으로 고려하지 않음
  Directional<·,·>: L=g(g-1) pairs
  Nondirectional(·,·): L=g(g-1)/2 pairs
  Strength or frequency of the interaction for a pair of actors (valued
  relation) 기록에는 적절치 않음

       Single relation: 그래프 안에 단일 relation
       Multiple relation: 그래프 안에 relation 여러 개, e.g. Friendship at
        Beginning<·,·>, Friendship and End<·,·>, Lives near(·,·)
(1) Chapter 3: Notation for social Network Data

   Sociometric notation
  The study of positive and negative affective(정서적인) relations. To
  measure affective relations, two-way matrices → 결국 sociomatrix
  로 표현됨 (page 82 표 참조, relation 당 matrix 한 개)
  Dichotomous: 2분법으로 표현 → relation set {0, 1} for C=2
  좋은 특징: valued relation 처리 가능, directional, nondirectional
  관계 모두 표현 가능
  단점: can not easily quantify or denote actor attributes(속성) e.g.
  ethnicity of the actors

      Single relation: xij = the value of the tie from ni to nj
      Multiple relation: xijr = the value of the tie from ni to nj on relation Xr e.g. x121
       = the value of the tie from n1 to n2 on relation X121=the value of the tie from
       n1 to n2 on relation X1 = 1
(1) Chapter 3: Notation for social Network Data

   Algebraic notation
  e.g. iFj 로 표현됨 (c.f. Sociometric: xijF=1)

  장점: multi-relational 관계 다루는 데에 가장 적합 -> 관계들 간의
  combinations 을 다루기 때문
  단점: 값이 항상 0 아니면 1이기 때문에(dichotomous) 감정이나 가
  치를 측정하지는 못함
(2) Chapter 4: Graphs and Matrices
(2) Chapter 4: Graphs and Matrices

   Density
  Δ = 2L / g(g-1) → no lines: Δ=0, all possible lines: Δ=1

   Walks, Trails, Paths
  Walks > Trails > Paths (page 106 참조)
  Walks: a sequence of nodes and lines, starting and ending with
  nodes, in which each node is incident with the lines following and
  preceding it in the sequence.
  Trails: walks with lines are distinct, though some nodes included
  more than once.
  Paths: walks with all nodes, all lines distinct
(2) Chapter 4: Graphs and Matrices

   Closed walks, Tours, Cycles
  Closed walks: A walk that begins and ends ant the same node.
  Tours: A closed walk, 라인이 적어도 한 번 이상은 쓰여야 함
  Cycles: 적어도 3개 이상 노드로 이루어진 closed walks, all lines
  and nodes(except the beginning and ending node) are distinct. →
  중요: balance and clusterability in signed graphs 에 쓰임

   Connected Graphs and Components
  Components: The connected Subgraphs in a graph, maximal
  connected subgraph
(2) Chapter 4: Graphs and Matrices

   Geodesics, Distance, Diameter
  Geodesics: A shortest path between two nodes
  (Geodesic) Distance: length of a geodesic between two nodes
  Diameter: diameter of a connected graph is the length of the largest
  geodesic between any pair of nodes. Important since it quantifies
  how far apart the farthest two nodes in the graph are.

   Cutpoints and Bridges
  Cutpoints: A node ni is a cutpoint if the number of components in
  the graphs that contains ni is fewer than the number of components
  in the subgraph that results from deleting ni from the graph.
  Bridges: The same to cutpoint in terms of a line
(2) Chapter 4: Graphs and Matrices
   Node- and Line- connectivity
  Cohesive graphs have many short geodesic, and small diameters
  relative their sizes.
      point-connectivity or node-connectivity of a graph, k(g) is
      the minimum number of k for which the graph has a k-node
      cut. Page 116, in Figure 4.12, k(g)=2

     line-connectivity or edge-connectivy, λ(g) is the minimum
     number λ fir which the grapg has a λ-line cut. Page 116,
     Figure 4.10 예제 관련 설명 이상함?

   Isomorphic(동형의) Graphs
  Isomorphism between graphs are important because if two graphs
  are isomorphic, then they are identical on all graph theoretic
  properties. Page 118 Figure 4.13 그래프 참조
(2) Chapter 4: Graphs and Matrices
   Special Graphs
  Trees: A graph that is connected and is acyclic. Trees are minimally
  connected graphs since every line in the graph is a bridge. The
  number of lines in a tree equals the number of nodes minus one, L=g-
  1. There is only one path between any two nodes.
  Forest: A graph is connected and contains no cycles, the number of
  lines = nodes – the number of components

   Bipartite Graphs and s-partite Graphs
  Bipartite Graphs: All lines are between nodes in different 2 subsets
  and no nodes in the same subset are adjacent. (Page 120 Figure 4.15
  및 Page 121 예제 참조), Complete bipartite: Bipartite with every nodes
  in one subsets is adjacent to every node in another subsets
  S-partite graphs: All lines are between nodes in different subsets and
  no nodes in the same subset are adjacent.
감사합니다!

More Related Content

What's hot

Lecture 5b graphs and hashing
Lecture 5b graphs and hashingLecture 5b graphs and hashing
Lecture 5b graphs and hashingVictor Palmar
 
Application of Matrices in real life | Matrices application | The Matrices
Application of Matrices in real life | Matrices application | The MatricesApplication of Matrices in real life | Matrices application | The Matrices
Application of Matrices in real life | Matrices application | The MatricesSahilJhajharia
 
Alg2 lesson 2.2 and 2.3
Alg2 lesson 2.2 and 2.3Alg2 lesson 2.2 and 2.3
Alg2 lesson 2.2 and 2.3Carol Defreese
 
Applications of graphs
Applications of graphsApplications of graphs
Applications of graphsTech_MX
 
Social network analysis study Chap 4.3
Social network analysis study Chap 4.3Social network analysis study Chap 4.3
Social network analysis study Chap 4.3Jaram Park
 
May 20, 2014
May 20, 2014May 20, 2014
May 20, 2014khyps13
 
How to read a character table
How to read a character tableHow to read a character table
How to read a character tablesourabh muktibodh
 
Construction of C3V character table
Construction of C3V  character tableConstruction of C3V  character table
Construction of C3V character tableEswaran Murugesan
 
Multiplication of matrices and its application in biology
Multiplication of matrices and its application in biologyMultiplication of matrices and its application in biology
Multiplication of matrices and its application in biologynayanika bhalla
 
DISTANCE AND SECTION FORMULA
DISTANCE AND SECTION FORMULADISTANCE AND SECTION FORMULA
DISTANCE AND SECTION FORMULAsumanmathews
 
Real interpolation method for transfer function approximation of distributed ...
Real interpolation method for transfer function approximation of distributed ...Real interpolation method for transfer function approximation of distributed ...
Real interpolation method for transfer function approximation of distributed ...TELKOMNIKA JOURNAL
 
Two Dimensional Shape and Texture Quantification - Medical Image Processing
Two Dimensional Shape and Texture Quantification - Medical Image ProcessingTwo Dimensional Shape and Texture Quantification - Medical Image Processing
Two Dimensional Shape and Texture Quantification - Medical Image ProcessingChamod Mune
 
An improved graph drawing algorithm for email networks
An improved graph drawing algorithm for email networksAn improved graph drawing algorithm for email networks
An improved graph drawing algorithm for email networksZakaria Boulouard
 
Equation of a straight line y b = m(x a)
Equation of a straight line y   b = m(x a)Equation of a straight line y   b = m(x a)
Equation of a straight line y b = m(x a)Shaun Wilson
 

What's hot (20)

Lecture 5b graphs and hashing
Lecture 5b graphs and hashingLecture 5b graphs and hashing
Lecture 5b graphs and hashing
 
Application of Matrices in real life | Matrices application | The Matrices
Application of Matrices in real life | Matrices application | The MatricesApplication of Matrices in real life | Matrices application | The Matrices
Application of Matrices in real life | Matrices application | The Matrices
 
Electrical Network Topology
Electrical Network TopologyElectrical Network Topology
Electrical Network Topology
 
Alg2 lesson 2.2 and 2.3
Alg2 lesson 2.2 and 2.3Alg2 lesson 2.2 and 2.3
Alg2 lesson 2.2 and 2.3
 
Alg2 lesson 2.2
Alg2 lesson 2.2Alg2 lesson 2.2
Alg2 lesson 2.2
 
Applications of graphs
Applications of graphsApplications of graphs
Applications of graphs
 
Social network analysis study Chap 4.3
Social network analysis study Chap 4.3Social network analysis study Chap 4.3
Social network analysis study Chap 4.3
 
May 20, 2014
May 20, 2014May 20, 2014
May 20, 2014
 
Graph theory
Graph theoryGraph theory
Graph theory
 
How to read a character table
How to read a character tableHow to read a character table
How to read a character table
 
Construction of C3V character table
Construction of C3V  character tableConstruction of C3V  character table
Construction of C3V character table
 
Multiplication of matrices and its application in biology
Multiplication of matrices and its application in biologyMultiplication of matrices and its application in biology
Multiplication of matrices and its application in biology
 
26 spanning
26 spanning26 spanning
26 spanning
 
DISTANCE AND SECTION FORMULA
DISTANCE AND SECTION FORMULADISTANCE AND SECTION FORMULA
DISTANCE AND SECTION FORMULA
 
Real interpolation method for transfer function approximation of distributed ...
Real interpolation method for transfer function approximation of distributed ...Real interpolation method for transfer function approximation of distributed ...
Real interpolation method for transfer function approximation of distributed ...
 
Two Dimensional Shape and Texture Quantification - Medical Image Processing
Two Dimensional Shape and Texture Quantification - Medical Image ProcessingTwo Dimensional Shape and Texture Quantification - Medical Image Processing
Two Dimensional Shape and Texture Quantification - Medical Image Processing
 
Lect14
Lect14Lect14
Lect14
 
An improved graph drawing algorithm for email networks
An improved graph drawing algorithm for email networksAn improved graph drawing algorithm for email networks
An improved graph drawing algorithm for email networks
 
Lect18
Lect18Lect18
Lect18
 
Equation of a straight line y b = m(x a)
Equation of a straight line y   b = m(x a)Equation of a straight line y   b = m(x a)
Equation of a straight line y b = m(x a)
 

Viewers also liked

Viewers also liked (20)

Chapter 7
Chapter 7Chapter 7
Chapter 7
 
Twitter
TwitterTwitter
Twitter
 
Chapter 8
Chapter 8Chapter 8
Chapter 8
 
Chapter 3
Chapter 3Chapter 3
Chapter 3
 
Chapter 6
Chapter 6Chapter 6
Chapter 6
 
Chapter 1
Chapter 1Chapter 1
Chapter 1
 
Chapter 9
Chapter 9Chapter 9
Chapter 9
 
Lets Give Back
Lets Give BackLets Give Back
Lets Give Back
 
Chapter 2
Chapter 2Chapter 2
Chapter 2
 
LL4 #21
LL4 #21LL4 #21
LL4 #21
 
letsgiveback.org
letsgiveback.orgletsgiveback.org
letsgiveback.org
 
Chapter 11
Chapter 11Chapter 11
Chapter 11
 
Good bye mr.chips
Good bye mr.chipsGood bye mr.chips
Good bye mr.chips
 
Timing advances
Timing advancesTiming advances
Timing advances
 
Tessituras: apresentação de Rosane Castro
Tessituras: apresentação de Rosane CastroTessituras: apresentação de Rosane Castro
Tessituras: apresentação de Rosane Castro
 
FBC Identity Plan
FBC Identity PlanFBC Identity Plan
FBC Identity Plan
 
CONTOH RPH PRASEKOLAH (bahasa inggeris)
CONTOH RPH PRASEKOLAH (bahasa inggeris)CONTOH RPH PRASEKOLAH (bahasa inggeris)
CONTOH RPH PRASEKOLAH (bahasa inggeris)
 
Inner join romario orcoapaza
Inner join romario orcoapazaInner join romario orcoapaza
Inner join romario orcoapaza
 
Phrasal verb
Phrasal verbPhrasal verb
Phrasal verb
 
Penglibatan ibu bapa dan komuniti di prasekolah
Penglibatan ibu bapa dan komuniti di prasekolahPenglibatan ibu bapa dan komuniti di prasekolah
Penglibatan ibu bapa dan komuniti di prasekolah
 

Similar to Social network analysis study ch3 4.2-120108

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
 
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
 
On algorithmic problems concerning graphs of higher degree of symmetry
On algorithmic problems concerning graphs of higher degree of symmetryOn algorithmic problems concerning graphs of higher degree of symmetry
On algorithmic problems concerning graphs of higher degree of symmetrygraphhoc
 
Skiena algorithm 2007 lecture10 graph data strctures
Skiena algorithm 2007 lecture10 graph data strcturesSkiena algorithm 2007 lecture10 graph data strctures
Skiena algorithm 2007 lecture10 graph data strctureszukun
 
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRYON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRYFransiskeran
 
Network Topology
Network TopologyNetwork Topology
Network TopologyHarsh Soni
 
Slides Chapter10.1 10.2
Slides Chapter10.1 10.2Slides Chapter10.1 10.2
Slides Chapter10.1 10.2showslidedump
 
Unit II_Graph.pptxkgjrekjgiojtoiejhgnltegjte
Unit II_Graph.pptxkgjrekjgiojtoiejhgnltegjteUnit II_Graph.pptxkgjrekjgiojtoiejhgnltegjte
Unit II_Graph.pptxkgjrekjgiojtoiejhgnltegjtepournima055
 
GraphSignalProcessingFinalPaper
GraphSignalProcessingFinalPaperGraphSignalProcessingFinalPaper
GraphSignalProcessingFinalPaperChiraz Nafouki
 
Graph theory ppt.pptx
Graph theory ppt.pptxGraph theory ppt.pptx
Graph theory ppt.pptxsaranyajey
 
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 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
 
Multiplex Networks: structure and dynamics
Multiplex Networks: structure and dynamicsMultiplex Networks: structure and dynamics
Multiplex Networks: structure and dynamicsEmanuele Cozzo
 
data structures and algorithms Unit 2
data structures and algorithms Unit 2data structures and algorithms Unit 2
data structures and algorithms Unit 2infanciaj
 

Similar to Social network analysis study ch3 4.2-120108 (20)

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
 
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
 
On algorithmic problems concerning graphs of higher degree of symmetry
On algorithmic problems concerning graphs of higher degree of symmetryOn algorithmic problems concerning graphs of higher degree of symmetry
On algorithmic problems concerning graphs of higher degree of symmetry
 
Skiena algorithm 2007 lecture10 graph data strctures
Skiena algorithm 2007 lecture10 graph data strcturesSkiena algorithm 2007 lecture10 graph data strctures
Skiena algorithm 2007 lecture10 graph data strctures
 
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRYON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
ON ALGORITHMIC PROBLEMS CONCERNING GRAPHS OF HIGHER DEGREE OF SYMMETRY
 
Graph in data structures
Graph in data structuresGraph in data structures
Graph in data structures
 
Network Topology
Network TopologyNetwork Topology
Network Topology
 
Slides Chapter10.1 10.2
Slides Chapter10.1 10.2Slides Chapter10.1 10.2
Slides Chapter10.1 10.2
 
Unit II_Graph.pptxkgjrekjgiojtoiejhgnltegjte
Unit II_Graph.pptxkgjrekjgiojtoiejhgnltegjteUnit II_Graph.pptxkgjrekjgiojtoiejhgnltegjte
Unit II_Graph.pptxkgjrekjgiojtoiejhgnltegjte
 
GraphSignalProcessingFinalPaper
GraphSignalProcessingFinalPaperGraphSignalProcessingFinalPaper
GraphSignalProcessingFinalPaper
 
Graph theory ppt.pptx
Graph theory ppt.pptxGraph theory ppt.pptx
Graph theory ppt.pptx
 
Graph Theory
Graph TheoryGraph Theory
Graph Theory
 
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
 
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
 
Multiplex Networks: structure and dynamics
Multiplex Networks: structure and dynamicsMultiplex Networks: structure and dynamics
Multiplex Networks: structure and dynamics
 
Siegel
SiegelSiegel
Siegel
 
data structures and algorithms Unit 2
data structures and algorithms Unit 2data structures and algorithms Unit 2
data structures and algorithms Unit 2
 
Graph Theory
Graph TheoryGraph Theory
Graph Theory
 
Network Theory
Network TheoryNetwork Theory
Network Theory
 
Graph.pptx
Graph.pptxGraph.pptx
Graph.pptx
 

Social network analysis study ch3 4.2-120108

  • 1. Social Network Analysis Study Chapter 3 - 4.2 Jan 8 2013, 박성규
  • 2. (1) Chapter 3: Notation for social Network Data  3가지 Notations  Graph theoretic: centrality and prestige methods, cohesive subgroup ideas, dyadic and triadic methods  Actor 와 Relation 으로 표현되는 scheme  Sociometric: study of structural equivalence and blockmodels  Social network 분야에서 가장 일반적인 notation  Algebraic: role and positional analyses and relational algebras  Multiple relation 에서 많이 사용됨
  • 3. (1) Chapter 3: Notation for social Network Data  Graph theoretic notation Set of actors: N={n1, n2, ng} Set of relations (lines, ordered pairs of actors, arcs, ties): L={l1,l2,lL} Graph는 수학적으로 두 개의 Set 으로 정리 가능: G={N, L} Self choices 는 일반적으로 고려하지 않음 Directional<·,·>: L=g(g-1) pairs Nondirectional(·,·): L=g(g-1)/2 pairs Strength or frequency of the interaction for a pair of actors (valued relation) 기록에는 적절치 않음  Single relation: 그래프 안에 단일 relation  Multiple relation: 그래프 안에 relation 여러 개, e.g. Friendship at Beginning<·,·>, Friendship and End<·,·>, Lives near(·,·)
  • 4. (1) Chapter 3: Notation for social Network Data  Sociometric notation The study of positive and negative affective(정서적인) relations. To measure affective relations, two-way matrices → 결국 sociomatrix 로 표현됨 (page 82 표 참조, relation 당 matrix 한 개) Dichotomous: 2분법으로 표현 → relation set {0, 1} for C=2 좋은 특징: valued relation 처리 가능, directional, nondirectional 관계 모두 표현 가능 단점: can not easily quantify or denote actor attributes(속성) e.g. ethnicity of the actors  Single relation: xij = the value of the tie from ni to nj  Multiple relation: xijr = the value of the tie from ni to nj on relation Xr e.g. x121 = the value of the tie from n1 to n2 on relation X121=the value of the tie from n1 to n2 on relation X1 = 1
  • 5. (1) Chapter 3: Notation for social Network Data  Algebraic notation e.g. iFj 로 표현됨 (c.f. Sociometric: xijF=1) 장점: multi-relational 관계 다루는 데에 가장 적합 -> 관계들 간의 combinations 을 다루기 때문 단점: 값이 항상 0 아니면 1이기 때문에(dichotomous) 감정이나 가 치를 측정하지는 못함
  • 6. (2) Chapter 4: Graphs and Matrices
  • 7. (2) Chapter 4: Graphs and Matrices  Density Δ = 2L / g(g-1) → no lines: Δ=0, all possible lines: Δ=1  Walks, Trails, Paths Walks > Trails > Paths (page 106 참조) Walks: a sequence of nodes and lines, starting and ending with nodes, in which each node is incident with the lines following and preceding it in the sequence. Trails: walks with lines are distinct, though some nodes included more than once. Paths: walks with all nodes, all lines distinct
  • 8. (2) Chapter 4: Graphs and Matrices  Closed walks, Tours, Cycles Closed walks: A walk that begins and ends ant the same node. Tours: A closed walk, 라인이 적어도 한 번 이상은 쓰여야 함 Cycles: 적어도 3개 이상 노드로 이루어진 closed walks, all lines and nodes(except the beginning and ending node) are distinct. → 중요: balance and clusterability in signed graphs 에 쓰임  Connected Graphs and Components Components: The connected Subgraphs in a graph, maximal connected subgraph
  • 9. (2) Chapter 4: Graphs and Matrices  Geodesics, Distance, Diameter Geodesics: A shortest path between two nodes (Geodesic) Distance: length of a geodesic between two nodes Diameter: diameter of a connected graph is the length of the largest geodesic between any pair of nodes. Important since it quantifies how far apart the farthest two nodes in the graph are.  Cutpoints and Bridges Cutpoints: A node ni is a cutpoint if the number of components in the graphs that contains ni is fewer than the number of components in the subgraph that results from deleting ni from the graph. Bridges: The same to cutpoint in terms of a line
  • 10. (2) Chapter 4: Graphs and Matrices  Node- and Line- connectivity Cohesive graphs have many short geodesic, and small diameters relative their sizes. point-connectivity or node-connectivity of a graph, k(g) is the minimum number of k for which the graph has a k-node cut. Page 116, in Figure 4.12, k(g)=2 line-connectivity or edge-connectivy, λ(g) is the minimum number λ fir which the grapg has a λ-line cut. Page 116, Figure 4.10 예제 관련 설명 이상함?  Isomorphic(동형의) Graphs Isomorphism between graphs are important because if two graphs are isomorphic, then they are identical on all graph theoretic properties. Page 118 Figure 4.13 그래프 참조
  • 11. (2) Chapter 4: Graphs and Matrices  Special Graphs Trees: A graph that is connected and is acyclic. Trees are minimally connected graphs since every line in the graph is a bridge. The number of lines in a tree equals the number of nodes minus one, L=g- 1. There is only one path between any two nodes. Forest: A graph is connected and contains no cycles, the number of lines = nodes – the number of components  Bipartite Graphs and s-partite Graphs Bipartite Graphs: All lines are between nodes in different 2 subsets and no nodes in the same subset are adjacent. (Page 120 Figure 4.15 및 Page 121 예제 참조), Complete bipartite: Bipartite with every nodes in one subsets is adjacent to every node in another subsets S-partite graphs: All lines are between nodes in different subsets and no nodes in the same subset are adjacent.