ConceptComplete

Graph Theory Basics - Key Properties

Graphs can be represented and analyzed using various mathematical structures. Understanding these representations and key invariants is essential for algorithmic applications and theoretical analysis.

DefinitionGraph Representations

Adjacency Matrix: For a graph G=(V,E)G = (V,E) with n=Vn = |V| vertices, the adjacency matrix AA is an n×nn \times n matrix where: A[i,j]={1if {vi,vj}E0otherwiseA[i,j] = \begin{cases} 1 & \text{if } \{v_i, v_j\} \in E \\ 0 & \text{otherwise} \end{cases}

For undirected graphs, AA is symmetric. For directed graphs, A[i,j]=1A[i,j] = 1 if there's an edge from viv_i to vjv_j.

Adjacency List: Store for each vertex a list of its neighbors. Space-efficient for sparse graphs.

Incidence Matrix: n×mn \times m matrix (m=Em = |E|) where entry (i,j)(i,j) indicates whether vertex ii is incident to edge jj.

DefinitionGraph Isomorphism

Graphs G1=(V1,E1)G_1 = (V_1, E_1) and G2=(V2,E2)G_2 = (V_2, E_2) are isomorphic, written G1G2G_1 \cong G_2, if there exists a bijection f:V1V2f: V_1 \to V_2 such that {u,v}E1\{u,v\} \in E_1 if and only if {f(u),f(v)}E2\{f(u), f(v)\} \in E_2.

Isomorphic graphs have the same structure under relabeling. Determining whether two graphs are isomorphic is a famous problem: not known to be in P, but not proven NP-complete.

ExampleMatrix Powers and Paths

The (i,j)(i,j) entry of AkA^k (where AA is the adjacency matrix) counts the number of walks of length kk from vertex ii to vertex jj.

Proof by induction: For k=1k=1, A1=AA^1 = A counts walks of length 1 (direct edges). If true for kk, then: (Ak+1)[i,j]=Ak[i,]A[,j](A^{k+1})[i,j] = \sum_{\ell} A^k[i,\ell] \cdot A[\ell,j]

This sums walks of length kk from ii to \ell, extended by one edge to jj.

DefinitionSubgraphs and Induced Subgraphs

A subgraph of G=(V,E)G = (V,E) is a graph H=(V,E)H = (V', E') where VVV' \subseteq V and EEE' \subseteq E.

An induced subgraph G[V]G[V'] includes all edges from EE with both endpoints in VV'.

A spanning subgraph has V=VV' = V.

A graph HH is a minor of GG if HH can be obtained from GG by deleting edges, deleting vertices, and contracting edges. Planar graphs are characterized by forbidden minors K5K_5 and K3,3K_{3,3}.

DefinitionGraph Invariants

Chromatic number χ(G)\chi(G): Minimum number of colors needed to color vertices so adjacent vertices have different colors.

Clique number ω(G)\omega(G): Size of the largest complete subgraph.

Independence number α(G)\alpha(G): Size of the largest independent set (no two vertices adjacent).

Edge connectivity κ(G)\kappa'(G): Minimum number of edges whose removal disconnects the graph.

Vertex connectivity κ(G)\kappa(G): Minimum number of vertices whose removal disconnects the graph.

Relationships: χ(G)ω(G)\chi(G) \geq \omega(G) and χ(G)α(G)V\chi(G) \cdot \alpha(G) \geq |V|.

Remark

The complement graph Gˉ\bar{G} has the same vertices as GG but edges where GG has non-edges. Many graph properties have interesting relationships with their complements. For instance, a graph is bipartite if and only if it contains no odd cycles, and Ramsey theory studies unavoidable substructures in graphs and their complements. The degree sequence (sorted list of all vertex degrees) is a graph invariant, but two non-isomorphic graphs can have the same degree sequence, so it's not a complete invariant. The spectrum (eigenvalues of the adjacency matrix) provides more information but still doesn't fully characterize graphs in general.

These properties and representations form the foundation for graph algorithms and deeper theoretical investigations.