Graph Theory | Computer Science

5.1 Definitions

A graph $G = (V, E)$ consists of a set of vertices $V$ and a set of edges $E \subseteq V \times V$ .

Simple graph: no loops, no multiple edges.
Directed graph (digraph): edges have direction.
Weighted graph: edges have weights.

The degree of a vertex $v$ , $\deg(v)$ Is the number of edges incident to $v$ .

Theorem 5.1 (Handshaking Lemma). $\sum_{v \in V} \deg(v) = 2|E|$ .

Proof. Each edge contributes 1 to the degree of each of its two endpoints. $\blacksquare$

Corollary 5.2. The number of vertices of odd degree is even.

5.2 Paths, Cycles, and Connectivity

A walk is a sequence of vertices where consecutive vertices are adjacent. A path is a walk With no repeated vertices. A cycle is a path that returns to its starting vertex.

A graph is connected if there is a path between every pair of vertices. A connected component Is a maximal connected subgraph.

Theorem 5.3. A graph with $n$ vertices and more than $(n-1)(n-2)/2$ edges is connected.

5.3 Trees

A tree is a connected acyclic graph. A forest is a disjoint union of trees.

Theorem 5.4. For a graph $G$ with $n$ vertices, the following are equivalent:

$G$ is a tree.
$G$ is connected and has $n - 1$ edges.
$G$ is acyclic and has $n - 1$ edges.
Between any two vertices, there is exactly one path.

Cayley”s Formula. The number of labelled trees on $n$ vertices is $n^{n-2}$ .

5.4 Planarity

A graph is planar if it can be drawn in the plane with no edge crossings.

Theorem 5.5 (Euler’s Formula for Planar Graphs). For a connected planar graph drawn in the plane With $V$ vertices, $E$ edges, and $F$ faces:

$V - E + F = 2$

Proof sketch. Build the graph edge by edge. Starting from a single vertex ( $V = 1$ , $E = 0$ , $F = 1$ ), The quantity $V - E + F = 2$ is preserved when adding an edge: if the edge connects two components, $E$ and $V$ each increase by 1; if it splits a face, $E$ and $F$ each increase by 1. $\blacksquare$

Corollary 5.6. For a simple planar graph with $V \geq 3$ : $E \leq 3V - 6$ .

Proof. Every face has at least 3 edges on its boundary, and every edge borders at most 2 faces, So $3F \leq 2E$ . By Euler’s formula, $F = 2 - V + E$ Giving $3(2 - V + E) \leq 2E$ I.e., $E \leq 3V - 6$ . $\blacksquare$

Corollary 5.7. $K_5$ and $K_{3,3}$ are not planar.

Proof. $K_5$ has $V = 5$ , $E = 10$ But $10 \gt 3(5) - 6 = 9$ . For $K_{3,3}$ , $V = 6$ , $E = 9$ . Since $K_{3,3}$ has no triangles, every face has at least 4 edges, giving $4F \leq 2E$ So $F \leq E/2 = 4.5$ . But $V - E + F = 2$ gives $F = 2 - 6 + 9 = 5 \gt 4.5$ . Contradiction. $\blacksquare$

Theorem 5.8 (Kuratowski’s Theorem). A graph is planar if and only if it does not contain a Subdivision of $K_5$ or $K_{3,3}$ as a subgraph.

A subdivision of an edge $uv$ replaces $uv$ with a path $u$ — $w$ — $v$ through a new vertex $w$ . A graph $H$ is a subdivision of $G$ if $H$ can be obtained from $G$ by subdividing edges.

Worked Example. Show that $K_{3,3}$ is not planar using Euler’s formula.

Solution

$K_{3,3}$ has $V = 6$ vertices and $E = 9$ edges. It is bipartite (partition sizes 3 and 3), so it Contains no triangles. Every face in a planar embedding must therefore be bounded by at least 4 edges, Giving $4F \leq 2E$ I.e., $F \leq 9/2 = 4.5$ .

But Euler’s formula gives $F = E - V + 2 = 9 - 6 + 2 = 5$ . Since $5 \gt 4.5$ No planar embedding Exists. $\blacksquare$

5.5 Graph Colouring

A proper $k$ -colouring assigns one of $k$ colours to each vertex such that adjacent vertices have Different colours. The chromatic number $\chi(G)$ is the minimum $k$ for which a proper $k$ -colouring Exists.

Theorem 5.9 (Four Colour Theorem). Every planar graph is 4-colourable.

Theorem 5.10 (Five Colour Theorem). Every planar graph is 5-colourable. (Easier to prove.)

Theorem 5.11 (Greedy Colouring Bound). $\chi(G) \leq \Delta(G) + 1$ where $\Delta(G)$ is the Maximum degree.

Theorem 5.12 (Brooks’ Theorem). If $G$ is connected and is neither a complete graph nor an odd Cycle, then $\chi(G) \leq \Delta(G)$ .

Chromatic polynomial. The chromatic polynomial $P(G, k)$ counts the number of proper $k$ -colourings of $G$ .

Theorem 5.13. $P(G, k)$ is a polynomial in $k$ .

Deletion-contraction recurrence. For any edge $e$ of $G$ :

$P(G, k) = P(G - e, k) - P(G / e, k)$

Where $G - e$ is $G$ with edge $e$ deleted, and $G / e$ is $G$ with $e$ contracted (its endpoints Merged).

Worked Example. Find the chromatic polynomial of $C_4$ (the 4-cycle).

Solution

Label the vertices of $C_4$ as $v_1, v_2, v_3, v_4$ with edges $v_1v_2$ , $v_2v_3$ , $v_3v_4$ , $v_4v_1$ .

Pick edge $e = v_1v_2$ .

$G - e$ is a path on 4 vertices (a tree): $P(G - e, k) = k(k-1)^3$ .

$G / e$ merges $v_1$ and $v_2$ Yielding $C_3$ (triangle): $P(C_3, k) = k(k-1)(k-2)$ .

Therefore $P(C_4, k) = k(k-1)^3 - k(k-1)(k-2) = k(k-1)[(k-1)^2 - (k-2)] = k(k-1)(k^2 - 3k + 3)$ .

Checking: $P(C_4, 2) = 2(1)(4 - 6 + 3) = 2$ (two 2-colourings: alternating), and $P(C_4, 3) = 3 \cdot 2 \cdot (9 - 9 + 3) = 18$ . $\blacksquare$

Worked Example. Find $\chi(K_n)$ and $\chi(K_{m,n})$ .

Solution

$K_n$ (complete graph on $n$ vertices): every pair of vertices is adjacent, so all $n$ vertices must Receive distinct colours. Hence $\chi(K_n) = n$ .

$K_{m,n}$ (complete bipartite graph): no two vertices within the same partition are adjacent, so we Can colour all vertices in the first partition with colour 1 and all in the second with colour 2. Hence $\chi(K_{m,n}) = 2$ (for $m, n \geq 1$ ).

Worked Example. Find the chromatic polynomial of $K_3$ (triangle).

Solution

Choose a colour for vertex 1: $k$ choices. Choose a colour for vertex 2 (different from vertex 1): $k - 1$ choices. Choose a colour for vertex 3 (different from both): $k - 2$ choices.

$P(K_3, k) = k(k-1)(k-2)$ .

Checking: $P(K_3, 2) = 2 \cdot 1 \cdot 0 = 0$ (not 2-colourable, as expected). $P(K_3, 3) = 6$ .

5.6 Euler and Hamilton Paths

An Euler path visits every edge exactly once. An Euler circuit is an Euler path that starts And ends at the same vertex.

Theorem 5.14. A connected graph has an Euler circuit if and only if every vertex has even degree. It has an Euler path (but not circuit) if and only if exactly two vertices have odd degree.

Proof (sufficiency). If every vertex has even degree, start at any vertex and follow edges, never Reusing an edge. Since all degrees are even, the walk can only terminate at the starting vertex, Forming a circuit $C$ . If $C$ uses all edges, we are done. Otherwise, remove $C$ ‘s edges; each Remaining component has all vertices of even degree (each lost an even number of incident edges). By induction, each component has an Euler circuit. Splicing these circuits into $C$ at shared Vertices yields an Euler circuit of the full graph. $\blacksquare$

Worked Example. Does $K_{2,3}$ have an Euler circuit? An Euler path?

Solution

$K_{2,3}$ has 5 vertices. The two vertices in the first partition each have degree 3 (connected to All three in the second partition). The three vertices in the second partition each have degree 2.

Vertices of odd degree: two (each of degree 3). Since exactly two vertices have odd degree, $K_{2,3}$ has an Euler path (starting at one odd-degree vertex, ending at the other) but not an Euler circuit.

A Hamilton path visits every vertex exactly once. A Hamilton circuit is a Hamilton path that Returns to the start.

Theorem 5.15 (Dirac’s Theorem). If $G$ is a simple graph with $n \geq 3$ vertices and $\deg(v) \geq n/2$ for every vertex, then $G$ has a Hamilton circuit.

Theorem 5.16 (Ore’s Theorem). If $G$ is a simple graph with $n \geq 3$ vertices and $\deg(u) + \deg(v) \geq n$ for every pair of non-adjacent vertices $u, v$ Then $G$ has a Hamilton Circuit.

Note that Dirac’s theorem is a corollary of Ore’s theorem.

Worked Example. Does $K_{2,3}$ have a Hamilton circuit?

Solution

$K_{2,3}$ has 5 vertices. A Hamilton circuit must visit all 5 vertices and return. Label the Partitions as $A = \\{a_1, a_2\\}$ and $B = \\{b_1, b_2, b_3\\}$ . Any cycle in a bipartite graph alternates Between the two partitions. A Hamilton cycle would alternate between $A$ and $B$ Requiring $|A| = |B|$ . But $|A| = 2 \neq 3 = |B|$ So no Hamilton circuit exists.

However, $K_{2,3}$ does have Hamilton paths (e.g., $a_1, b_1, a_2, b_2, b_3$ — wait, this doesn’t Alternate properly). Actually, in $K_{2,3}$ edges only exist between $A$ and $B$ . A path must alternate $A, B, A, B, \ldots$ or $B, A, B, A, \ldots$ . A Hamilton path visits all 5 vertices, so it has the Form $a, b, a, b, a$ (length 5, starting and ending in $A$ ) or $b, a, b, a, b$ (length 5, starting And ending in $B$ ). The first requires 3 vertices from $A$ But $|A| = 2$ . The second requires 3 Vertices from $B$ And $|B| = 3$ . So a Hamilton path exists: e.g., $b_1, a_1, b_2, a_2, b_3$ .

Worked Example. Let $G$ have vertices $\\{1, 2, 3, 4, 5\\}$ and edges $12, 23, 34, 45, 51, 13, 35$ . Does $G$ have an Euler circuit or Euler path?

Solution

Degrees: $\deg(1) = 3$ , $\deg(2) = 2$ , $\deg(3) = 4$ , $\deg(4) = 2$ , $\deg(5) = 3$ . Two vertices (1 and 5) have odd degree. Since exactly two vertices have odd degree, $G$ has an Euler Path (starting at 1, ending at 5) but no Euler circuit.

One Euler path: $1 \to 2 \to 3 \to 4 \to 5 \to 3 \to 1 \to 5$ . All 7 edges are used exactly once. ✓

Algorithm (Hierholzer’s). To find an Euler circuit: start at any vertex, follow unused edges Until returning to the start. If unused edges remain, find a vertex on the current circuit with Unused edges, find a subtour, and splice it in. Repeat until all edges are used.

whereas Euler Paths/circuits can be determined in polynomial time using the degree condition. Do not confuse the two.

5.7 Matching Theory

A matching $M$ in a graph $G = (V, E)$ is a set of pairwise disjoint edges (no two share an Endpoint). A vertex is matched if it is an endpoint of an edge in $M$ ; otherwise it is unmatched. A perfect matching covers every vertex.

Theorem 5.17 (Hall’s Marriage Theorem, 1935). Let $G = (V, E)$ be a bipartite graph with Partitions $X$ and $Y$ . There exists a matching that covers every vertex in $X$ if and only if for Every subset $S \subseteq X$

$|N(S)| \geq |S|$

Where $N(S) = \\{y \in Y : \exists\, x \in S\; \mathrm{with{}\; xy \in E\\}$ is the neighbourhood of $S$ .

Proof (necessity). If a matching covers $X$ Each $x \in S$ is matched to a distinct $y \in N(S)$ So $|N(S)| \geq |S|$ .

Proof (sufficiency by induction on $|X|$ ). Base case $|X| = 1$ : Hall’s condition gives $|N(\\{x\\})| \geq 1$ So $x$ has a neighbour, and we can match $x$ to it.

Inductive step. Consider two cases.

Case 1: For every nonempty proper subset $S \subsetneq X$ , $|N(S)| \gt |S|$ . Pick any edge $xy$ . In $G' = G - \\{x, y\\}$ Hall’s condition still holds (removing one element from each side preserves the Strict inequality). By the induction hypothesis, $X \setminus \\{x\\}$ can be matched in $G'$ . Adding $xy$ Gives the desired matching.

Case 2: There exists a nonempty proper $T \subsetneq X$ with $|N(T)| = |T|$ . Match $T$ to $N(T)$ By the induction hypothesis. In $G'' = G - (T \cup N(T))$ For any $S \subseteq X \setminus T$ $N_{G''}(S) = N_G(S \cup T) \setminus N(T)$ So

$|N_{G''}(S)| = |N_G(S \cup T)| - |N(T)| \geq |S \cup T| - |T| = |S|$

Where the inequality uses Hall’s condition on $S \cup T$ in $G$ . By the induction hypothesis, $X \setminus T$ can be matched in $G''$ . Combining with the matching on $T$ gives the result. $\blacksquare$

Worked Example. Let $X = \\{a, b, c, d\\}$ and $Y = \\{1, 2, 3, 4, 5\\}$ with edges $a$ — $1,2$ ; $b$ — $1,2$ ; $c$ — $2,3$ ; $d$ — $3,4,5$ . Does a matching covering $X$ exist?

Solution

Check Hall’s condition for every subset $S \subseteq X$ :

$|S| = 1$ : each vertex has at least 1 neighbour. ✓
$|S| = 2$ : $N(\\{a, b\\}) = \\{1, 2\\}$ , $|N| = 2$ . $N(\\{a, c\\}) = \\{1, 2, 3\\}$ , $|N| = 3$ . Similarly all pairs have $|N| \geq 2$ . ✓
$|S| = 3$ : $N(\\{a, b, c\\}) = \\{1, 2, 3\\}$ , $|N| = 3$ . $N(\\{a, c, d\\}) = \\{1, 2, 3, 4, 5\\}$ , $|N| = 5$ . All triples have $|N| \geq 3$ . ✓
$|S| = 4$ : $N(X) = \\{1, 2, 3, 4, 5\\}$ , $|N| = 5 \geq 4$ . ✓

Hall’s condition is satisfied, so a matching exists. One such matching: $a$ — $1$ , $b$ — $2$ $c$ — $3$ , $d$ — $4$ .

5.8 Network Flows

A flow network is a directed graph $G = (V, E)$ with a source $s$ A sink $t$ And a capacity function $c : E \to \mathbb{R}_{\geq 0}$ . A flow $f : E \to \mathbb{R}_{\geq 0}$ Satisfies:

Capacity constraint: $0 \leq f(e) \leq c(e)$ for all $e \in E$ .
Flow conservation: for all $v \in V \setminus \\{s, t\\}$ $\sum_{e\; \mathrm{into{}\; v} f(e) = \sum_{e\; \mathrm{out\; of{}\; v} f(e)$ .

The value of a flow is $|f| = \sum_{e\; \mathrm{out\; of{}\; s} f(e) - \sum_{e\; \mathrm{into{}\; s} f(e)$ .

An s-t cut $(S, T)$ partitions $V$ into $S$ (containing $s$ ) and $T$ (containing $t$ ). The capacity of the cut is $c(S, T) = \sum_{u \in S, v \in T} c(uv)$ .

Theorem 5.18 (Max-Flow Min-Cut Theorem). In any flow network, the maximum value of a flow from $s$ to $t$ equals the minimum capacity of an $s$ - $t$ cut.

Proof sketch. Let $f^*$ be a maximum flow. Define the residual graph $G_f$ with the same Vertices and residual capacity $c_f(uv) = c(uv) - f(uv)$ for forward edges and $c_f(vu) = f(uv)$ For backward edges. Let $S$ be the set of vertices reachable from $s$ in $G_{f^*}$ via edges with Positive residual capacity. Since $f^*$ is maximum, $t \notin S$ (otherwise we could augment the Flow). The cut $(S, V \setminus S)$ has capacity exactly $|f^*|$ (all forward edges are saturated, All backward edges have zero flow). Therefore $|f^*| = c(S, V \setminus S) \geq$ minimum cut Capacity $\geq |f^*|$ Giving equality. $\blacksquare$

Theorem 5.19 (Integrality Theorem). If all capacities are integers, there exists a maximum flow Where every $f(e)$ is an integer.

Corollary 5.20. If all capacities are integers, the maximum flow value is an integer.

The Ford—Fulkerson method repeatedly finds augmenting paths in the residual graph and pushes Flow along them. When capacities are integers, each augmentation increases the flow by at least 1, Guaranteeing termination.

flowchart TD
    A[5_Graph Theory] --> B[Key Concepts]
    A --> C[Core Principles]
    A --> D[Practical Applications]
    B --> E[Fundamental definitions]
    C --> F[Design patterns]
    D --> G[Real-world usage]

Intuition

Graph theory studies networks of vertices connected by edges. Paths, cycles, and connectivity describe graph structure. Trees are connected acyclic graphs, fundamental for hierarchies and search. Planar graphs can be drawn without edge crossings. Graph coloring assigns labels satisfying constraints, modeling scheduling and resource allocation. Euler and Hamiltonian paths visit edges or vertices exactly once, connecting to classic optimization problems.

Common Mistakes

Confusing Euler and Hamilton paths. An Euler path visits every edge exactly once, while a Hamilton path visits every vertex exactly once. Euler paths have a simple degree-based characterisation (0 or 2 odd-degree vertices), but Hamilton path existence is NP-complete. Do not assume a graph with an Euler path also has a Hamilton path, or vice versa.

Misapplying Euler’s formula for planarity. Euler’s formula $V - E + F = 2$ applies only to connected planar graphs. Students often apply it to disconnected graphs or forget that faces include the unbounded outer face. Also, satisfying Euler’s formula is necessary but not sufficient for planarity; Kuratowski’s theorem provides the complete characterisation.

Incorrect chromatic polynomial evaluation. When computing $P(G, k)$ , students sometimes forget that the chromatic polynomial counts proper colourings (adjacent vertices must differ). Evaluating at $k = 2$ gives 0 for non-bipartite graphs, which is a useful sanity check. Also, the deletion-contraction recurrence must be applied carefully to avoid double-counting.

Cross-References

[[1-algorithms/graph-algorithms]] - Graph algorithm implementations
[[1-discrete-mathematics/4_combinatorics]] - Counting graph structures
[[2-systems/networking]] - Network topology graphs
[[1-algorithms/data-structures]] - Graph data structures
Discrete Mathematics
Algorithm Implementation

Graph Theory | Computer Science

5.1 Definitions

5.2 Paths, Cycles, and Connectivity

5.3 Trees

5.4 Planarity

5.5 Graph Colouring

5.6 Euler and Hamilton Paths

Intuition

Common Mistakes

Cross-References

See Also