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A biconnected component is a maximal connected subgraph with no cut vertex of its own. We use the block convention that also includes each bridge in a graph with its two endpoint vertices, and each isolated vertex, as a biconnected component. A biconnected component with at least three vertices stays connected after any single vertex deletion. Distinct biconnected components share at most one vertex; the vertices belonging to more than one biconnected component are exactly the cut vertices. The shared-$A$ example has two biconnected components, its two triangles in a graph. The joined-$CD$ example has three: $ABC$, the bridge in a graph $CD$ with its endpoints, and $DEF$. The edges partition into these pieces, while the shared cut vertices occur in several pieces. ^[ref]

A 2-core can contain bridges in a graph, and even vertices that lie on no cycle in a graph. Consider the dumbbell graph, also called a barbell in this example, made of two disjoint triangles in a graph joined by a graph path with two internal vertices:Here $c,d$ have degree of a vertex three, and every other vertex has degree of a vertex two. No deletion is possible, so the whole graph is its 2-core. Yet $cx,xy,yd$ are bridges in a graph: deleting any one separates the two triangles. None belongs to a cycle in a graph, and $x,y$ belong to no cycle in a graph either. Keeping only edges on graph cycles would incorrectly discard the entire joining graph path.

Every cycle in a graph does survive pruning, because each of its vertices keeps its two cycle graph neighbours. The barbell shows that the converse fails: the 2-core can be larger than the union of all graph cycles. Its condition is local minimum degree, not protection against a single connection failing.

A biconnected component is a different object: a maximal connected subgraph with no cut vertex of its own. Under the block convention used here, the barbell has five biconnected components: its two triangles and its three individual joining edges, each with its endpoints. Its cut vertices are $c,x,y,d$. A 2-connected component with at least three vertices must remain connected after any single vertex is deleted; the whole barbell fails that test. An informal phrase such as "biconnected core" therefore needs its own definition and must not be substituted for 2-core. Even an isolated edge is a block under this convention, although its 2-core is empty.

The usefulness of the 2-core is this precise separation of local persistence from global robustness. In a connected graph with nonempty 2-core, the deleted vertices form a forest, with each of its connected components of a graph attached by exactly one edge to the 2-core. Otherwise a deleted graph cycle, or a graph path joining two surviving attachment points, would itself have survived. Stripping these attached trees exposes a smaller structure for studying graph cycles, graph excess and the graph kernel, while preserving the joining bridges in a graph that organize that structure.

A cut vertex, also called an articulation point, is a vertex $v$ of an undirected graph whose deletion increases the number of connected components of a graph. Write $k(G)$ for that count, including isolated vertices, and $G-v$ for the graph obtained by removing $v$ and every incident edge. Then the test is $k(G-v)>k(G)$, even when the original graph is disconnected. By contrast, deleting a bridge in a graph removes just one edge and keeps both endpoint vertices. These are different failure models. ^[ref]

First take two triangles in a graph, $ABC$ and $ADE$, sharing only the vertex $A$. This simple graph hasHere $XY$ abbreviates the unordered pair $\{X,Y\}$. Removing $A$ leaves the two connected components of a graph $\{B,C\}$ and $\{D,E\}$, so $k(G-A)=2>1=k(G)$. Removing any other vertex leaves a connected graph. Thus $A$ is the only cut vertex. Every edge lies on a triangle in a graph, so deleting any single edge still leaves a graph path between its endpoints along the other two edges. There are no bridges in a graph.

The generator computes the adjacency lists and checks every vertex deletion, giving the following complete table:

Now take two disjoint triangles in a graph, $ABC$ and $DEF$, joined by the edge $CD$:Deleting only $CD$ preserves all six vertices and leaves connected components of a graph $\{A,B,C\}$ and $\{D,E,F\}$: $CD$ is the only bridge in a graph. Deleting $C$ also removes $AC$ and $BC$, leaving connected components of a graph $\{A,B\}$ and $\{D,E,F\}$. Deleting $D$ instead leaves $\{A,B,C\}$ and $\{E,F\}$. Thus $C,D$ are exactly the cut vertices.

Again the adjacency lists and all vertex deletion counts are computed directly:

For a disconnected example, adjoin an isolated vertex $Z$ to the first graph. Now $k(G)=2$, deleting $A$ gives $k(G-A)=3$, and deleting $Z$ gives $k(G-Z)=1$. The original graph was already disconnected, but $A$ is still a cut vertex; $Z$ is not. The empty graph has count zero, so deleting the only vertex of a one-vertex graph does not create a cut vertex. Nor is either endpoint of a single isolated edge a cut vertex, although that edge is a bridge in a graph.

In a communications network, a cut vertex models a router whose failure separates surviving routers. The shared-$A$ example shows why having an alternative graph path around each individual edge does not protect against failure of the common router. An additional edge from $B$ to $D$ removes this particular vulnerability: after deleting $A$, $B,D$ still join the two sides. A bridge in a graph instead models a vulnerable link. This distinction tells us whether to add another cable between existing devices or a route avoiding an indispensable device; it describes connectivity, rather than traffic capacity or failure probabilities.

The beautiful structural consequence is that a graph splits into biconnected components joined at cut vertices. Within each nontrivial robust piece, a single vertex failure cannot separate the other vertices; between pieces, the shared cut vertices reveal the bottlenecks. Also, a Hamiltonian graph has no cut vertex: deleting any vertex from its Hamilton cycle leaves a Hamiltonian path through all remaining vertices.

Historically, Tarjan's June 1972 paper developed depth-first search as a tool for finding biconnected components in time linear in the numbers of vertices and edges. ^[ref] In his 1983 recollection, Tarjan credited Hopcroft with the original linear-time biconnected component algorithm during their collaboration beginning in summer 1970, and described the published version as a refinement. The historical lesson is how a carefully organized traversal turns many separate deletion experiments into one computation. ^[ref]

For a finite simple graph that is an undirected graph, the DFS lowpoint of $v$ is the smallest DFS discovery time reachable by following zero or more discovery edges down its rooted tree, then at most one non-discovery edge to an ancestor. Staying at $v$ is allowed. Ignore the reverse traversal of the discovery edge to the parent in a rooted tree: it is the same edge, not an escape route. Start with $\mathrm{low}(v)=\mathrm{disc}(v)$. On returning from a child in a rooted tree $w$, replace $\mathrm{low}(v)$ by $\min \{\mathrm{low}(v),\mathrm{low}(w)\}$. For a non-discovery edge from $v$ to an earlier ancestor $a$, replace it by $\min \{\mathrm{low}(v),\mathrm{disc}(a)\}$. This summarizes how far upward the explored branch can reconnect. ^[ref]

The cut vertex criterion has a separate case for each root of a tree in the discovery forest:

Why does equality matter? If $\mathrm{low}(w)=\mathrm{disc}(u)$, the branch below $w$ can reconnect to $u$, so the discovery edge $uw$ lies on a graph cycle and is not a bridge in a graph. Deleting the vertex $u$ destroys that reconnection too. If $u$ has a parent in a rooted tree, the branch is then separated from that parent in a rooted tree, explaining the non-strict cut vertex test. For a root of a tree, there is no earlier side to separate from; distinct discovery branches cannot have an edge between them, since depth-first search would have explored them as one branch. This gives the two-children in a rooted tree rule. These are proof outlines of the criteria. ^[ref][ref]

In the joined-$CD$ example with DFS discovery times $1,2,3,4,5,6$, the DFS lowpoints in label order are $1,1,1,4,4,4$. For $C$ and its child in a rooted tree $D$, $4>3$: $CD$ is a bridge in a graph, and $C$ is a cut vertex. For $D$ and its child in a rooted tree $E$, $4=4$: $D$ is a cut vertex, although $DE$ is not a bridge in a graph. The root of a tree $A$ has only one child in a rooted tree, $B$, so it is not a cut vertex. In the shared-$A$ example, the root of a tree $A$ has two children in a rooted tree, $B$ and $D$, and is a cut vertex, while every DFS lowpoint is $1$ and no edge passes the strict bridge in a graph test.

Restart depth-first search at every undiscovered vertex to cover a disconnected graph, applying the root of a tree rule separately in each connected component of a graph. With adjacency lists and constant-cost label operations, computing all DFS lowpoints, cut vertices and bridges in a graph has time complexity $O(|V|+|E|)$ in Big O notation, because each vertex is discovered once and each edge is inspected twice. The simple parent-skipping rule assumes a simple graph. In a multigraph, skip only the actual parent edge, identified individually: a parallel edge to the same parent in a rooted tree is a valid reconnection and can prevent the first edge from being a bridge in a graph. The criteria here concern undirected graphs; a directed graph requires a different connectivity analysis.

A Hamilton cycle, also called a Hamiltonian cycle, is a cycle in a graph containing every vertex of the graph. Here the convention is a finite simple graph whose edges are undirected, with $n=|V|\ge 3$. A traversal is a sequencewhere $v_0,\dots ,v_{n-1}$ are all the distinct vertices and $\{v_i,v_{i+1}\}\in E$ for $0\le i<n$. Thus "once" means once before the closing return: the start appears again at the end, and there are exactly $n$ traversed edges. The graph may contain additional unused edges.^[ref]

A Hamiltonian path visits all vertices once without requiring a return. Removing an edge from a Hamilton cycle gives a Hamiltonian path; a particular Hamiltonian path closes only if its endpoints are adjacent. An Euler circuit instead traverses every edge exactly once and can revisit vertices midway. The two requirements are independent, as these original labelled examples show.

For the complete graph $K_4$, use $V=\{A,B,C,D\}$ and $E=\{AB,AC,AD,BC,BD,CD\}$, where $AB$ abbreviates the unordered pair $\{A,B\}$. The route $A,B,C,D,A$ is a Hamilton cycle: it visits every vertex and uses four of the six edges, omitting $AC$ and $BD$. It is not an Euler circuit, and the graph admits no Euler circuit at all: every vertex degree is three, contradicting the Euler circuit criterion. The crossing of the drawn diagonals is not another vertex.

Conversely, take two triangles in a graph sharing only $X$, with $V=\{X,A,B,C,D\}$ and $E=\{XA,AB,BX,XC,CD,DX\}$. The route $X,A,B,X,C,D,X$ is an Euler circuit, using all six edges once; the vertex degrees are $4,2,2,2,2$. It cannot be a Hamilton cycle, since $X$ occurs midway as well as at the start and end. More strongly, no Hamilton cycle exists: $X$ is a cut vertex, and deleting it leaves the two separate edges $AB$ and $CD$. Nevertheless, $A,B,X,C,D$ is a Hamiltonian path, whose endpoints are not adjacent.

A Hamiltonian graph must be a connected graph and have minimum degree $\delta (G)\ge 2$: each vertex has two distinct neighbours on its Hamilton cycle. It also has no cut vertex. Indeed, deleting any vertex from the Hamilton cycle leaves a graph path through every remaining vertex, so the remaining graph is connected. These are quick ways to rule out a Hamilton cycle.^[ref]

Even these conditions together are not sufficient: the complete bipartite graph $K_{2,3}$ is connected, has minimum degree two and no cut vertex, but has no Hamilton cycle. Every graph cycle in a bipartite graph alternates between its two parts, using equally many vertices from each; it cannot use all two and all three.

In the other direction, Dirac's theorem gives a sufficient condition: a finite simple graph with $n\ge 3$ and $\delta (G)\ge n/2$ has a Hamilton cycle. Failure of this inequality proves nothing by itself: the cycle graph $C_5$ has a Hamilton cycle despite $\delta (C_5)=2<5/2$.^[ref]

Let $v_1\cdots v_k$ be a longest path. Every neighbour of either endpoint lies on it. Among the $k-1$ indices $i=1,\dots ,k-1$, mark those with $v_1{v}_{i+1}$ an edge and those with $v_i{v}_k$ an edge. Their total number isso some index has both marks. The edges at that index close the path into the cycle graphThe minimum-degree hypothesis also forces the graph to be a connected graph: each component would otherwise have at least $n/2+1$ vertices. If $k<n$, an edge from this cycle to an outside vertex would give a longer path by opening the cycle at its incident vertex. Hence $k=n$, proving the Dirac theorem and Hamiltonicity.

The bound is sharp. For $n=2m$, take two disjoint copies of $K_m$, with minimum degree $m-1$. For $n=2m+1$, take two copies of $K_{m+1}$ sharing exactly one vertex. The minimum degree is $m$, but the shared vertex is a cut vertex, which no Hamiltonian graph can have. Both constructions have