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.
Adjacency Matrix: For a graph with vertices, the adjacency matrix is an matrix where:
For undirected graphs, is symmetric. For directed graphs, if there's an edge from to .
Adjacency List: Store for each vertex a list of its neighbors. Space-efficient for sparse graphs.
Incidence Matrix: matrix () where entry indicates whether vertex is incident to edge .
Graphs and are isomorphic, written , if there exists a bijection such that if and only if .
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.
The entry of (where is the adjacency matrix) counts the number of walks of length from vertex to vertex .
Proof by induction: For , counts walks of length 1 (direct edges). If true for , then:
This sums walks of length from to , extended by one edge to .
A subgraph of is a graph where and .
An induced subgraph includes all edges from with both endpoints in .
A spanning subgraph has .
A graph is a minor of if can be obtained from by deleting edges, deleting vertices, and contracting edges. Planar graphs are characterized by forbidden minors and .
Chromatic number : Minimum number of colors needed to color vertices so adjacent vertices have different colors.
Clique number : Size of the largest complete subgraph.
Independence number : Size of the largest independent set (no two vertices adjacent).
Edge connectivity : Minimum number of edges whose removal disconnects the graph.
Vertex connectivity : Minimum number of vertices whose removal disconnects the graph.
Relationships: and .
The complement graph has the same vertices as but edges where 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.