5-图论

Introduction

• Graph $G=(V,E)$
  • order $|V|$
• incident $u\sim v$: u and v are neighbors
  • walk $W = (u=v_0, v_1, \dots, v_k = v)$
    • open walk: $u\neq v$
    • closed walk: $u=v$
  • trail: $u$-$v$ walk in which no edge is traversed more than once
  • circuit: a closed trial of length 3 or more
  • path: $u$-$v$ walk in which no vertices are repeated
  • cycle: a circuit that repeats no vertex, except for the first and last
• connected: $\exists$ $u$-$v$ path
  • distance $d(u,v)$: exists smallest $u$-$v$ path $P=(u=v_0,\dots,v_k=v)$
  • geodesic $u$-$v$ of length $d(u,v)$
  • diamemter $\text{diam}(G)=\max_{u,v}(d(u,v))$
• subgraph
  • proper $G'=(V',E'),V'\subsetneq V$ or $E'\subsetneq E$
  • spanning $G'=(V,E'),E'\subseteq E$
  • induced $G'=(V',E')$: $\forall u,v\in V',$ if $uv\in E$, then $uv\in E'$
  • edge-induced
  • component: 最大连通子图
• Common Graphs
  • trivial graph: $|V|=1$
  • $P_n$ : path (graph)
  • $C_n$: circle (graph)
  • $K_n$ : complete graph
  • bipartite graph: contains no odd cycle
    • $K_{s,t}$: complete bipartite graph
    • $K_{1,t}$ or $K_{s,1}$: star
  • k-partite graph
    • $K_{i_1,\dots,i_k}$: complete k-partite graph
  • n-cubes (hypercubes): $Q_n = Q_{n-1} \times K_2$, $Q_1 = K_2$
• Operation
  • Complement: $\overline{G}=(V,K_n-E)$
  • Union: $G_1 \cup G_2=(V_1\cup V_2,E_1\cup E_2)$
  • Join: $G+H=(V_1\cup V_2, E_1\cup E_2\cup (V_1\times V_2))$
  • Cartesian product $G\times H=(V(G)\times V(H),E'),((u,v),(x,y))\in E'$ if $u=x,vy\in E(H)$ or $v=y,ux\in E$
• multigraph: $E$ is a multiset
  • parallel edges: join the same pair of vertices
• pseudograph: $(u,u)\in E$
• oriented graph: if $(u,v)\in E$, $(v,u)\notin E$
  • oritentation of G
• Storage
  • adjacent matrix
  • edge set（可以链式前向星实现）

Digraph

• digraph (directed graph): $(v,u)\neq (u,v)$
• $(u,v)$: $u$ adjacent to $v$, $v$ adjacent from $u$
• symmetric: $(u,v)\in E\iff(v,u)\in E$
• underlying graph: replace direction and parallel edges
• tournament: orientation of complete graph
  • transitive: $(u,v),(v,w)\in T,(u,w)\in T$
  • every tournament contains a Hamiltonian path
  • Hamiltonian iff strong
• weakly connected: underlying graph is connected
• strongly connected: $\forall u,v,\exists P$ from $u$ to $v$ and vice versa
  • strongly connected component (SCC)
• Semiconnected Digraph: $u\sim v$ or $v\sim u$
  • Toposort + edges $(v_i,v_{i+1})$ exsits

Isomorphism

• isomorphic $G\cong H$: $\exists$ 1-1 $\phi:V(G)\rightarrow V(H)$ such that $uv\in E(G)\Rightarrow\phi(u)\phi(v)\in E(H)$
  • self-complementary: $G \cong \overline G$
  • isomorphism is an equivalence relation of the set of all graphs
• automorphism: isomorphism from $G$ to $G$
• $\text{Aut}(H)$: automorphism group of $G$ (under composition)
  • $u$ and $v$ are similar: they are in same orbit
  • vertex-transitive: $G$ contains a single orbit
• Frucht's Theorem: For every finite group $A$, there exists a graph $G$ such that $\text{Aut}(G)$ is isomorphic to $A$
• recognizable: a parameter of $G$ can be determined from $G-v,v\in V(G)$
  • $|V|$
  • $|E|$ for $|V|\geq3$
  • degree sequence for $|V|\geq 3$
• reconstructible: $G$ can be uniquely determined (up to isomorphism) from subgraphs $G-v_i$
  • Reconstruction Conjecture: $G$ is reconstructible for $|V|\geq 3$

Trees

• bridge $e$: $G$ is connected, $G-\{e\}$ is disconnected
  • bridge $\iff$ $!\exists n, C_n\subseteq G, e\in C_n$
  • nontrivial connected graph has a strong orientation $\iff$ no bridges in $G$
• acyclic graph(forests): $!\exists n,C_n\subseteq G$
• tree: acyclic graph + connected
• spanning tree of G
  • cycle property
  • cut property
  • weight: $w(H)=sum_{e\in E(H)}w(e)$
• Kruskal's Algorithm: $O(E\lg V)$

sort G.E # into nondecreasing order by weight
for e in E:
  if find_set(u) != find_set(v):
     ans += {u,v}
     merge(u,v)

• Prim's Algorithm: $O(E + V\lg V)$

for u in G.V:
  u.key = inf
  u.pi = NIL
s.key = 0
Q = G.V
while Q:
  u = Extract_Min(Q)
  for v in G.Adj[u]:
    if v in Q and w(u,v) < v.key:
      v.pi = u
      v.key = w(u,v)

• Carley's Theorem: the spanning tree of $K_n$ of order n is $n^{n-2}$
• Matrix Tree Theorem: The spanning tree of an arbitrary graph is any cofactor of matrix

Degrees

• $\deg v=|\{u:u\sim v\}|=N(v)$
  • isolated vertex: $\deg v=0$
  • end-vertex(leaf): $\deg v=1$
• $\delta(G)=\min_{v\in G}(\deg v)$
• $\Delta(G)=\max_{v\in G}(\deg v)$
• The First Theorem of Graph Theory: $\sum_{v\in V(G)} deg(v) = 2|E|$
• $r$-regular graph: $\delta(G) = \Delta(G) = r$
  • $r$-regular graph of order $n$ exists iff at least one of $r$ and $n$ is even ($H_{r,n}$: Harary graphs)
  • cubic graph(3-regular graph): $K_4, K_{3,3}, Q_3$, Petersen graph
• degree sequence: non-increasing
• graphical: finite and can be a degree sequence
• Havel-Hakimi Theorem: non-increaing sequence $s:d_1,d_2,...,d_n$ where $d_1\geq 1$ is graphical iff $s_1:d_2-1,d_3-1,...,d_{d_1+1}-1,d_{d_1+2},...,d_n$ is graphical.
• irregular graph: $\forall u,v\in V,\deg u\neq \deg v$
  • The Party Theorem: At any party, there is a pair of people who have the same number of friends present
  • No nontrivial graph is irregular
• $F$-degree of $v$: number of copies of $F$ in $G$ contain $v$
  • $G$ contain $m$ copies of $F$, then $\sum_{u\in V(G)}\deg_F v =km$
  • $F$-(ir)regular
• adjacency matrix $A$: $V\times V$
• incidence matrix $B$: $V\times B$

Connectivity

Vertex

• cut-vertex: $G$ is connected and $G-\{v\}$ is disconnected
• nonseparable graph (biconnected): a nontrivial connected graph with no cut-vertices
• block (bicomponent)
  • maximal nonseparable subgraph of $G$
  • equivalence class defined by $R, e R f$ if $e,f$ lie on a common cycle
• vertex-cut: a set $U$ of vertices of $G$ such that $G-U$ is disconnected
  • minimum vertex-cut
• vertex-connectivity: $\kappa(G)=\min|U|,G-U$ disconnected
  • equals $\max_{u\sim v}|\{\text{disjoint u-v path}\}|$
  • $k$-connected: $\kappa(G)\geq k$
  • $2$-connected: no vertex-cut

Edge

• edge-cut: $G$ is connected and $G-\{e\}$ is disconnected
  • minimum edge-cut
• edge-connectivity: $\lambda(G)$
  • $k$-edge-connected
• $\kappa(G)\leq\lambda(G)\leq\delta(G)$
  • cubic graph: $\kappa(G)=\lambda(G)$

Menger's Theorem

• $u$-$v$ separating set $S$: $u\not\sim v$ and belong to seperate components of $G-S$
  • minimum $u$-$v$ separating set
• internal vertex of $u$-$v$ path $P$: $w\in P,w\neq v,w\neq u$
• internally disjoint paths: A collection of u-v paths that every two of them have no vertices in common other than u and v
• Menger's Theorem: minimum cardinality of $u$-$v$ separating set = maximum number of internally disjoint $u$-$v$ paths in G
  • induction on the size of graph, discussion on the property of separating set
• Whitney's Theorem: $k$-connected $\iff\forall u,v$ there are at least $k$ internally disjoint $u$-$v$ paths

Traversability

• Eulerian circuit: a circuit contains every edge of G
  • Eulerian graph: graph G contians an Eulerian circuit
  • A nontrivial connected graph G is Eulerian iff every vertex of G has even degree
  • (directed) $\text{od} v=\text{id} u$
• Eulerian trail: an open trail that contains every edge of G
  • $K\ddot{o}nigsberg$ Bridge Problem
  • A connected graph G contains an Eulerian trail iff exactly two vertices of G have odd degree.
• Hamiltonian cycle: a cycle in a graph $G$ that contains every vertex of $G$
  • Hamiltonian graph
• Hamiltonian path: a path in $G$ contains every vertex of $G$
• $k(G)$: number of components in G
  • (neccessary) Hamiltonian Graph: $\forall S,k(G-S)\leq|S|$
  • (sufficient) $n\geq3,\forall u\not\sim v,\deg u+\deg v\geq n$, then $G$ is Hamiltonian
• closure $C(G)$: the graph obtained from $G$(order $n$) by recursively joining pairs of nonadjacent vertices whose degree sum $\geq n$ until no such pair remains
  • (neccessary) Hamiltonian Graph: $C(G)$ is Hamiltonian

Search

• BFS: $\Theta(V+E)$

for u in vertices:
  u.pi = NIL
  u.visited = False
s.visited = True
Q = {s}
while Q:
  u = Q.pop()
  for v in G.Adj[u]:
    if not u.visitied:
      v.pi = u
      v.visited = True
      Q.push(u)

• DFS: $\Theta(V+E)$

# all colored white
def dfs(u):
  u.visited = True
  # color gray
  for v in G.Adj[v]:
    if not v.visited:
      v.pi = u
      v.visited = True
      dfs(v)
# color black

• DFS properties
  • Parenthesis theorem
  • Nesting of descendant's intervals
  • White-path theorem
• Classification of Edges
  • Tree edge: edge in $G_\pi$ (white)
  • Back edge: ancestor (gray)
  • Forward edge: descendant (black)
  • Cross edge (black)
  • There is no Forward edges and Cross edge in undirected graphs
• directed acyclic graph(DAG): no back edge
• Toposort on dag: sort vertices in descending order of their finish times
  • component graph: dag of its SCCs
• Kosaraju's Algorithm: $\Theta(V+E)$

dfs(G) # compute u.f for each vertex u
dfs(G^T) # in order of decreasing u.f in main loop
# SCC is trees in depth-first forest

• Tarjan's Algorithm: $\Theta(V+E)$
  • cut-vertex(undirected): $\exists v$ son of $u$, low[v] $\geq$ dnf[u]
  • bridge(undirected): $\exists v$ son of $u$, low[v] > dnf[u]
  • directed: SCC

def tarjan_dfs(u):
  t += 1
  dfn[u] = low[u] = t
  Stack.push(u)
  for v in G.Adj[u]:
    if not v.visited:
      tarjan_dfs(v)
      low[u] = min(low[u], low[v])
    elif v in Stack:
      low[u] = min(low[u], dfn[v])
  if dfn[u] == low[u]:
    while v != Stack.top():
      Stack.pop()

Distance

• distance
  • positive
  • reflexivity
  • symmetric
  • triangle inequatity
• metric space: $(V(G), d)$
• eccentricity: $e(v)=\max_{u\in V(G)}d(u,v)$
• radius: $\text{rad}(G)=min_{v\in V(G)}e(v)$
  • center: $Cen(G)=\{v:e(v)=\text{rad}(G)\}$
    • self-centered: $Cen(G) = G$
    • center is subgraph of block
• diameter: $\text{diam}(G)=max_{v\in V(G)}e(v)$
  • periphery: $Per(G)=\{v:e(v)=\text{diam}(G)\}$
• $\text{rad}(G)\leq \text{diam}(G) \leq 2\text{rad}(G)$
• eccentric vertex of $u=\{v:d(u,v)=e(u)\}$
  • $Ecc(G)=\{v:\exists u,d(u,v)=e(u)\}$
• boundary vertex of a vertex $u=\{v:\forall w\sim v,d(u,w)\leq d(u,v)\}$
  • peripheral $\Rightarrow$ eccentric $\Rightarrow$ boundary
• complete vertex (extrem or simplicial vertex): the subgraph of G induced by the neighbors of v is complete
  • complete $\Rightarrow$ boundary
• interior vertex: $\forall u, \exists w, d(u,w)=d(u,v)+d(v,w)$
  • $Int(G)$: subgraph induced by interior vertices
  • boundary $\Rightarrow$ not interior (connected graph)

Shortest Path

• simple path -> path
• path -> walk
• in DAG or only positive edges: path = simple path
• shortest path: walk
  • single source shortest path(sssp):
  • all pair shortest path(assp)
• longest path: simple path, NP
• relaxation

def relax(u,v,w):
  if v.d > u.d + w(u,v):
     v.d = u.d + w(u,v)
     v.pi = u

• Properties
  • Triangle inequality
  • Upper-bound property
  • No-path property
  • Convergence property
  • Path-relaxation property
  • Predecessor-subgraph property
• Bellman-Ford Algorithm: $\Theta(VE)$

s.d = 0
for i in range(|G.V|):
  for e in G.E:
    relax(e.u, e.v, w)
for i in range(|G.V|):
  if v.d > u.d + w(u,v):
    return False
return True

• DAG shortest path: $\Theta(V+E)$
  • critical path: longest path through dag

for u in G.topo:
  for v in G.Adj[u]:
    relax(u,v,w)

• Dijkstra Algorithm: all edges are nonnegative
  • Array: $O(V^2)$
  • Min-heap: $O(E\lg V)$ for sparse graph $E=o(\frac{V^2}{\lg V})$
  • Fib-heap: $O(E+V\lg V)$

Q = G.V
while Q:
  u = Extract_Min(Q)
  for v in G.Adj[u]:
    relax(u, v, w)

• matrix multiplication and repeated squaring: $\Theta(n^3\lg n)$
• Floyed-Warshall Algorithm: $\Theta(V^3)$
  • dynamic programming on first $k$ vertices as intermediate vertices

for k in range(n):
  for i in range(n):
    for j in range(n):
      d[i][j] = min(d[i][j], d[i][k] + d[k][j])

• Johnson's Algorithm: $\Theta(V^2\lg V+VE)$
  • reweighting: $\hat \omega(u,v)=\omega(u,v)+h(u)-h(v)$

Matching

• independent set(matching) $F$: $\forall e_1,e_2\in F,e_1\cap e_2=\emptyset$
• Theorem: partite graph $G$ has partite sets $U$ and $W$ such that $r=|U|\leq|W|$. Then $G$ contains a matching of cardinality $r$ iff $G$ satisfies Hall's condition.
  • $N(X)=\{v:\exists u\in X,u\sim v\}$
  • Hall's condition: $\forall X\neq \emptyset, X\subseteq U,|N(X)|\geq|X|$
  • equivalent
    • a system of distinct representatives
    • marriage theory
• Hungarian algorithm: $O(VE)$
  • alternate path: unmatched edge - matched edge - unmatched edge - $\cdots$ - unmatched edge
  • argumentation path: alternate path covering unmatched vertex
• perfect matching: a graph of order $2k$ has a matching $M$ of cardinality $k$
  • Every $r$-regular bipartite graph has a perfect matching
• edge independence number(maximum matching): $\alpha'(G)=\max |M|$
• edge covering number $\beta'(G)=$ cardinality of minimum edge cover
  • cover: a vertex and an incident edge are said to cover each other
  • edge cover: a set of edges of G that covers all vertices of G
• vertice independence number $\alpha(G)$: maximum cardinality of an independent set of vertices in G
  • vertice independence: no two vertices in the set are adjacent
  • maximum independent set
• vertex covering number $\beta(G)$
• Gallai Identity: $\forall G,|G|=n$, containing no isolated vertices, $\alpha'(G)+\beta'(G)=n, \alpha(G)+\beta(G)=n$
• Konig Theorem: bipartite graph $G$ has $\alpha'(G)=\beta(G),\alpha(G)=\beta'(G)$

Flow

• Flow network: $G=(V,E)$
  • Capacity constraint: $\forall u,v\in V$, we require $0\leq f(u,v)\leq c(u,v)$
  • Flow conservation: $\forall u\in V-\{s,t\}$, $\sum_{v\in V}f(v,u)=\sum_{v\in V}f(u,v)$
• special cases
  • antiparallel edges: split
  • multiple sources and sinks: add source or sink
• flow: $|f(u,v)|=\sum_{v\in V}f(s,v)-\sum_{v\in V}f(v,s)$
• net flow: $f(S,T)=\sum_{u\in S}\sum_{v\in T}f(u,v)-\sum_{u\in S}\sum_{v\in T}f(v,u)$
• capacity of cut: $c(S,T)=\sum_{u\in S}\sum_{v\in T}c(u,v)$
• Max-flow Min-cut Theorem: The maximum flow $=$ the minimum cut capacity
• Ford-Fulkerson Method
  • residual networks $G_f$: $E_f=\{(u,v)\in V\times V:c_f(u,v)>0\}$ $c_f(u,v)=\begin{cases}c(u,v)-f(u,v) & (u,v)\in E\newline f(v,u) & (v,u)\in E \newline 0 & o.w.\end{cases}$
  • augmentation: $f'$ is flow of residual network, if $(u,v)\in E$, $(f\uparrow f')(u,v)=f(u,v)+f'(u,v)-f'(v,u)$
    • $|f\uparrow f'|=|f|+|f'|$
  • augmentation path: simple path from $s$ to $t$ in residual network

# initial flow f to 0
while augmenting_path in residual_network:
  augment(f,augmenting_path)
return f

• Basic Ford-Fulkerson Algorithm: $O(|f^*|E)$ arbitrary with integer

while augmenting_path in residual_network:
  c_f(p) = min([c_f(u,v) for (u,v) in p])
  for (u,v) in p:
    if (u,v) in E
      (u,v).f += c_f(p)
    else:
      (v,u).f -= c_f(p)

• Edmonds-Karp Algorithm: $O(VE^2)$
  • search augmenting path by BFS in residual network, where each edge has unit distance
• Push-relabeled algorithms: $O(V^2E)$
• relabel-to-front algorithm: $O(V^3)$

Factorization

• $k_o(G)$: number of odd components of $G$
• factor $F$: spanning subgraph of $G$
• factorable: $E(G)=\bigsqcup_{i=1}^{k}E(F_i)$
• $F$-factorable: factorable and $F_i\cong F$
• $r$-factorable: factorable and $F_i$ is $r$-regular graph
• $1$-factorable: perfect matching
  • Tutte's Theorem: $\forall S\in V(G),k_0(G-S)\leq|S|$, then $G$ contains a $1$-factor
  • Petersen's Theorem: Every 3-regular bridgeless graph contains a 1-factor
  • Every $r$-regular bipartite graph is $1$-factorable
• $2$-factorable: cyclic factorization
  • $G$ is 2-factorable iff $G$ is $r$-regular, $r$ is positive even integer
• Hamiltonian-factorization
  • $\forall k\geq1$, $K_{2k+1}$ is Hamiltonian-factorable
  • $\forall k\geq1$, $K_{2k}$ can be factored into $k-1$ Hamiltonian-cycles and a $1$-factor
• Kirkman's Schoolgirl Problem: Is there a $5K_3$-factorization of $K_{15}$

Decomposition

• decomposable: $E(G)=\bigsqcup_{i=1}^{k}E(H_i)$
  • if $H$ is spanning subgraph, then it is factorization
• $H$-decomposable: $H_i\cong H$
• graceful labeling $f:V(G)\rightarrow\mathbb{Z}_m$:
  • one-to-one
  • $f'(e)=|f(u)-f(v)|$ is one-to-one.
• graceful graph: $G$ possessing a graceful labeling
• Conjecture: Every tree is graceful
• girth: $\min_{C_n\subseteq G} n$
• $g$-cage: minimal 3-regular graph that has girth $g\geq3$
  • $g=3,K_4$
  • $g=4,K_{3,3}$
  • $g=5$ Petersen
  • $g=6$ Heawoo
  • $g=7$ McGee
  • $g=8$ Tutte-Coxeter
  • $g\leq8$, the g-cages are all sole

Embedding

Plane

• plane graph: $G$ is drawed on(embeded into) a plane with no edge crossing
  • regions $R$: connected area divied by plane graph
  • exterior regions: a region without bounding
  • boundary of $R$: subgraph corresponding to a region
• planar graph: $\exists P$ is a plane graph, $G\cong P$
• Euler Identity: a connected plane graph of order n, number of edge m and r regions, $n-m+r=2$
  • a planar graph of order $n\geq3$ then $m\leq3n-6$ ($2m\geq3r$)
  • every planar graph contains a vertex of degree $5$ or less
• maximal planar: planar but any addition of an edge results in nonplanar graph
• Kuratowski Theorem: G is planar $\iff$ it does not contain a subgraph of $K_5,K_{3,3}$ or subdivision of $K_5, K_{3,3}$
  • subdivision: inserting vertex of degree 2 into $G$

Surface

• $S_k$ surface of genus $k$: a surface with $k$ handle
  • $S_0$: sphere
    • planar graph can be embedded into a sphere
  • $S_1$: torus
• $\gamma(G)$: minimal integer $k$ that $G$ can be embedded into $S_k$
• $A$ and $A'$ are in the same region if there is a line connecting there without crossing edges or vertices of $G$
• 2-cell embedding: every region is 2-cell embedding
  • 2-cell: a region in which any closed curves can shrink into a node continuously
• Theorem: If $G$ is 2-cell embedded into $S_k$ then $n-m+r=2-2k$
  • $n\geq3,\gamma(G)\geq\frac{m}{6}-\frac{n}{2}+1$
• $\gamma(K_n)=\lceil\frac{(n-3)(n-4)}{12}\rceil$

Minor

• $G'$ is got from $G$ by contracting edge $e$ (or identifying the vertices u,v)
• minor: $H$ can be got be a series of edge contraction from $G$
• $G$ is a subdivision of $H\Rightarrow H$ is a minor of $G$
• $H$ is a minor of $G\Rightarrow\gamma(H)\leq\gamma(G)$
• Wagner Theorem: $G$ is planar $\iff$ $K_5,K_{3,3}$ is not a minor of $G$
• The Graph Minor Theorem: For any infinite set $S$ of graphs, there are two distinct graph in $S$ that one is a minor of the other

Coloring

• clique: a complete subgraph of graph
• clique number $\omega(G)$: the largest order of clique in $G$
  • $\beta(G)=\omega(\overline G)$
• coloring (proper coloring) $c:V(G)\rightarrow\mathbb{N},\forall u\sim v,c(u)\neq c(v)$
  • color class: the independet set division corresponding to a coloring
• $\chi(G)$: chromatic number, the minimal number of color to color G
  • $k$-colorable: $\chi(G)\leq k$
  • $\chi(G)=1\iff G$ has no edges
  • $\chi(G)=2\iff G$ is a nonempty partite graph
  • $\chi(C_{2n+1})=3$
  • $\chi(G)=4$ for triangle-free graph (Grotzsch graph)
  • Four Color Theorem: For every planar graph G, $\chi(G)\leq4$
• $\chi(G)$ inequality
  • $\chi(G)\geq\omega(G)$
  • $\chi(G)\geq\frac{n}{\beta(G)}$
  • $\chi(G)\leq 1+\Delta(G)$
  • Brook Theorem: $G$ is odd circle or complete graph, then $\chi(G)\leq \Delta(G)$
  • $\chi(G)\leq 1+\max(\delta(H))$, $H$ is all possible induced subgraph
• perfect: $\forall H\subseteq G,\chi(H)=\omega(H)$
  • perfect iff complement is perfect
• edge coloring $c:E(G)\rightarrow\mathbb{N},\forall e_1\cap e_2\neq\emptyset,c(e_1)\neq c(e_2)$
• $\chi'(G)$: edge chromatic number(chromatic index)
  • Divide $G$ into minimum number of 1-regulation graph
• Vizing Theorem: $\chi'(G)=\Delta G$ or $=\Delta G+1$
  • If $m>\frac{(n-1)\Delta G}{2}$ then $\chi'(G)=1+\Delta(G)$
  • Konig Theorem: For nonempty partite graph, $\chi'(X)=\Delta(G)$

Ramsey Numbers

• Ramsey's Theorem: $\forall k+1\geq 3$ positive integers $t,n_1,n_2,\cdots,n_k,\exists n>0,\forall S\subseteq \mathbb{Z}_{n},|S|=t$ is colored with one of $k$ colors, then $\exists S$ containing $n_i$ elements such that every $t$-element subset of $S$ is colored $i$
  • ($t=2$) $\forall k\geq 2,n_1,n_2,\cdots,n_k,\exists n>0,E(K_n)$ is colored by $k$ colors that $\exists i$, complete subgraph $K_{n_i}$ is colored $i$
• Ramsey's number $r(F_1,F_2)$: the smallest positive integer n such that if every edge of Kn is colored red or blue in any manner whatsoever, then either a red F1 or a blue F2 is produced

Domination