How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
A finite graph is bipartite if and only if it has no odd cycle
Statement
A finite simple graph is bipartite if and only if it contains no cycle of odd length.
Facts & Assumptions
Given: A finite simple graph .
A bipartition is an ordered pair of disjoint subsets with and every edge running between its two parts (A bipartite graph and a proper two-colouring of its vertices).
Every odd closed walk contains an odd cycle (Every closed walk of odd length contains a cycle of odd length).
Path-reachability is an equivalence relation, and its equivalence classes are the connected components; hence two vertices lie in the same component exactly when a path joins them. Every such path has a length and therefore a parity (Being joined by a walk or path is the same equivalence relation on the vertex set, Connected graphs and connected components defined by the existence of vertex paths, Graph distance within a component, eccentricity, diameter and girth, including the acyclic convention).
The connected components are nonempty, cover the vertex set, and are pairwise equal or disjoint (The connected components of a graph partition its vertex set and are its maximal connected subgraphs).
Proof
Suppose has a bipartition . Along any walk the successive vertices alternate between and , so a closed walk returns to its initial part only after an even number of edges. In particular, every cycle has even length.
Conversely, suppose has no odd cycle. Then by [L1] it has no odd closed walk.
Let be an arbitrary connected component. It is nonempty by [L2], so fix a root . Put equal to the vertices joined to by an even-length path and equal to those joined to by an odd-length path. Every vertex of lies in at least one of these sets by [F2]. This construction is vacuous when the graph has no components.
The sets and are disjoint: an even to path followed by the reverse of an odd to path would be an odd closed walk, contrary to step 1.2.
Every edge inside runs between and . Otherwise choose paths from to and from to having the common parity of their class. Traversing the first path, the edge , and the reverse of the second path gives an odd closed walk, contrary to step 1.2.
By [L2], the connected components partition the finite vertex set and therefore form a finite family. Repeating this construction for each component and taking and to be the unions of their respective parts gives disjoint sets with . The endpoints of an edge are joined by that one-edge path and hence lie in one component by [F2], so every edge runs between the two global parts. Thus is a bipartition.
Step 1.1 proves that bipartite graphs have no odd cycle, and steps 1.2 to 5.1 prove the converse.
Depends on
- A bipartite graph and a proper two-colouring of its vertices
- Every closed walk of odd length contains a cycle of odd length
- Being joined by a walk or path is the same equivalence relation on the vertex set
- The connected components of a graph partition its vertex set and are its maximal connected subgraphs
- Connected graphs and connected components defined by the existence of vertex paths
- Graph distance within a component, eccentricity, diameter and girth, including the acyclic convention
Used by
Dependency tree · next 3 levels
Direct dependencies and their dependencies through the next three levels: 30 results over 16 levels. An arrow runs from a result to what uses it, and this result sits at the bottom with a heavier outline. Click the chart to enlarge it.
Sources
- R. Diestel, Graph Theory, Chapter 1 preview (standard reference, not scraped)