In graph theory, a branch of mathematics, an **indifference graph** is an undirected graph constructed by assigning a real number to each vertex and connecting two vertices by an edge when their numbers are within one unit of each other.^{ [1] } Indifference graphs are also the intersection graphs of sets of unit intervals, or of properly nested intervals (intervals none of which contains any other one). Based on these two types of interval representations, these graphs are also called **unit interval graphs** or **proper interval graphs**; they form a subclass of the interval graphs.

The finite indifference graphs may be equivalently characterized as

- The intersection graphs of unit intervals,
^{ [1] } - The intersection graphs of sets of intervals no two of which are nested (one containing the other),
^{ [1] }^{ [2] } - The claw-free interval graphs,
^{ [1] }^{ [2] } - The graphs that do not have an induced subgraph isomorphic to a claw
*K*_{1,3}, net (a triangle with a degree-one vertex adjacent to each of the triangle vertices), sun (a triangle surrounded by three other triangles that each share one edge with the central triangle), or hole (cycle of length four or more),^{ [3] } - The incomparability graphs of semiorders,
^{ [1] } - The undirected graphs that have a linear order such that, for every three vertices ordered ––, if is an edge then so are and
^{ [4] } - The graphs with no
**astral triple**, three vertices connected pairwise by paths that avoid the third vertex and also do not contain two consecutive neighbors of the third vertex,^{ [5] } - The graphs in which each connected component contains a path in which each maximal clique of the component forms a contiguous sub-path,
^{ [6] } - The graphs whose vertices can be numbered in such a way that every shortest path forms a monotonic sequence,
^{ [6] } - The graphs whose adjacency matrices can be ordered such that, in each row and each column, the nonzeros of the matrix form a contiguous interval adjacent to the main diagonal of the matrix.
^{ [7] } - The induced subgraphs of powers of chordless paths.
^{ [8] } - The leaf powers having a leaf root which is a caterpillar.
^{ [8] }

For infinite graphs, some of these definitions may differ.

Because they are special cases of interval graphs, indifference graphs have all the properties of interval graphs; in particular they are a special case of the chordal graphs and of the perfect graphs. They are also a special case of the circle graphs, something that is not true of interval graphs more generally.

In the Erdős–Rényi model of random graphs, an -vertex graph whose number of edges is significantly less than will be an indifference graph with high probability, whereas a graph whose number of edges is significantly more than will not be an indifference graph with high probability.^{ [9] }

The bandwidth of an arbitrary graph is one less than the size of the maximum clique in an indifference graph that contains as a subgraph and is chosen to minimize the size of the maximum clique.^{ [10] } This property parallels similar relations between pathwidth and interval graphs, and between treewidth and chordal graphs. A weaker notion of width, the clique-width, may be arbitrarily large on indifference graphs.^{ [11] } However, every proper subclass of the indifference graphs that is closed under induced subgraphs has bounded clique-width.^{ [12] }

Every connected indifference graph has a Hamiltonian path.^{ [13] } An indifference graph has a Hamiltonian cycle if and only if it is biconnected.^{ [14] }

Indifference graphs obey the reconstruction conjecture: they are uniquely determined by their vertex-deleted subgraphs.^{ [15] }

As with higher dimensional unit disk graphs, it is possible to transform a set of points into their indifference graph, or a set of unit intervals into their unit interval graph, in linear time as measured in terms of the size of the output graph. The algorithm rounds the points (or interval centers) down to the nearest smaller integer, uses a hash table to find all pairs of points whose rounded integers are within one of each other (the fixed-radius near neighbors problem), and filters the resulting list of pairs for the ones whose unrounded values are also within one of each other.^{ [16] }

It is possible to test whether a given graph is an indifference graph in linear time, by using PQ trees to construct an interval representation of the graph and then testing whether a vertex ordering derived from this representation satisfies the properties of an indifference graph.^{ [4] } It is also possible to base a recognition algorithm for indifference graphs on chordal graph recognition algorithms.^{ [14] } Several alternative linear time recognition algorithms are based on breadth-first search or lexicographic breadth-first search rather than on the relation between indifference graphs and interval graphs.^{ [17] }^{ [18] }^{ [19] }^{ [20] }

Once the vertices have been sorted by the numerical values that describe an indifference graph (or by the sequence of unit intervals in an interval representation) the same ordering can be used to find an optimal graph coloring for these graphs, to solve the shortest path problem, and to construct Hamiltonian paths and maximum matchings, all in linear time.^{ [4] } A Hamiltonian cycle can be found from a proper interval representation of the graph in time ,^{ [13] } but when the graph itself is given as input, the same problem admits linear-time solution that can be generalized to interval graphs.^{ [21] }^{ [22] }

List coloring remains NP-complete even when restricted to indifference graphs.^{ [23] } However, it is fixed-parameter tractable when parameterized by the total number of colors in the input.^{ [12] }

In mathematical psychology, indifference graphs arise from utility functions, by scaling the function so that one unit represents a difference in utilities small enough that individuals can be assumed to be indifferent to it. In this application, pairs of items whose utilities have a large difference may be partially ordered by the relative order of their utilities, giving a semiorder.^{ [1] }^{ [24] }

In bioinformatics, the problem of augmenting a colored graph to a properly colored unit interval graph can be used to model the detection of false negatives in DNA sequence assembly from complete digests.^{ [25] }

- Threshold graph, a graph whose edges are determined by sums of vertex labels rather than differences of labels
- Trivially perfect graph, interval graphs for which every pair of intervals is nested or disjoint rather than properly intersecting

In graph theory, an **interval graph** is an undirected graph formed from a set of intervals on the real line, with a vertex for each interval and an edge between vertices whose intervals intersect. It is the intersection graph of the intervals.

In the mathematical discipline of graph theory, the **line graph** of an undirected graph *G* is another graph L(*G*) that represents the adjacencies between edges of *G*. L(*G*) is constructed in the following way: for each edge in *G*, make a vertex in L(*G*); for every two edges in *G* that have a vertex in common, make an edge between their corresponding vertices in L(*G*).

In graph theory, an **edge coloring** of a graph is an assignment of "colors" to the edges of the graph so that no two incident edges have the same color. For example, the figure to the right shows an edge coloring of a graph by the colors red, blue, and green. Edge colorings are one of several different types of graph coloring. The **edge-coloring problem** asks whether it is possible to color the edges of a given graph using at most k different colors, for a given value of k, or with the fewest possible colors. The minimum required number of colors for the edges of a given graph is called the **chromatic index** of the graph. For example, the edges of the graph in the illustration can be colored by three colors but cannot be colored by two colors, so the graph shown has chromatic index three.

In the mathematical area of graph theory, a **chordal graph** is one in which all cycles of four or more vertices have a *chord*, which is an edge that is not part of the cycle but connects two vertices of the cycle. Equivalently, every induced cycle in the graph should have exactly three vertices. The chordal graphs may also be characterized as the graphs that have perfect elimination orderings, as the graphs in which each minimal separator is a clique, and as the intersection graphs of subtrees of a tree. They are sometimes also called **rigid circuit graphs** or **triangulated graphs**.

In graph theory, the **complement** or **inverse** of a graph G is a graph H on the same vertices such that two distinct vertices of H are adjacent if and only if they are not adjacent in G. That is, to generate the complement of a graph, one fills in all the missing edges required to form a complete graph, and removes all the edges that were previously there. It is not, however, the set complement of the graph; only the edges are complemented.

In graph theory, a **cograph**, or **complement-reducible graph**, or ** P_{4}-free graph**, is a graph that can be generated from the single-vertex graph

In graph theory, the **treewidth** of an undirected graph is a number associated with the graph. Treewidth may be defined in several equivalent ways: the size of the largest vertex set in a tree decomposition of the graph, the size of the largest clique in a chordal completion of the graph, the maximum order of a haven describing a strategy for a pursuit-evasion game on the graph, or the maximum order of a bramble, a collection of connected subgraphs that all touch each other.

In graph theory, a **path decomposition** of a graph *G* is, informally, a representation of *G* as a "thickened" path graph, and the **pathwidth** of *G* is a number that measures how much the path was thickened to form *G*. More formally, a path-decomposition is a sequence of subsets of vertices of *G* such that the endpoints of each edge appear in one of the subsets and such that each vertex appears in a contiguous subsequence of the subsets, and the pathwidth is one less than the size of the largest set in such a decomposition. Pathwidth is also known as **interval thickness**, **vertex separation number**, or **node searching number**.

In graph theory, a branch of mathematics, a **split graph** is a graph in which the vertices can be partitioned into a clique and an independent set. Split graphs were first studied by Földes and Hammer, and independently introduced by Tyshkevich and Chernyak (1979).

In graph theory, an area of mathematics, a **claw-free graph** is a graph that does not have a claw as an induced subgraph.

In graph theory, the **clique-width** of a graph is a parameter that describes the structural complexity of the graph; it is closely related to treewidth, but unlike treewidth it can be bounded even for dense graphs. It is defined as the minimum number of labels needed to construct by means of the following 4 operations :

- Creation of a new vertex
*v*with label*i* - Disjoint union of two labeled graphs
*G*and*H* - Joining by an edge every vertex labeled
*i*to every vertex labeled*j*, where - Renaming label
*i*to label*j*

In graph theory, a branch of discrete mathematics, a **distance-hereditary graph** is a graph in which the distances in any connected induced subgraph are the same as they are in the original graph. Thus, any induced subgraph inherits the distances of the larger graph.

In graph theory, a **trivially perfect graph** is a graph with the property that in each of its induced subgraphs the size of the maximum independent set equals the number of maximal cliques. Trivially perfect graphs were first studied by but were named by Golumbic (1978); Golumbic writes that "the name was chosen since it is trivial to show that such a graph is perfect." Trivially perfect graphs are also known as **comparability graphs of trees**, **arborescent comparability graphs**, and **quasi-threshold graphs**.

In graph theory and theoretical computer science, the **longest path problem** is the problem of finding a simple path of maximum length in a given graph. A path is called simple if it does not have any repeated vertices; the length of a path may either be measured by its number of edges, or by the sum of the weights of its edges. In contrast to the shortest path problem, which can be solved in polynomial time in graphs without negative-weight cycles, the longest path problem is NP-hard and the decision version of the problem, which asks whether a path exists of at least some given length, is NP-complete. This means that the decision problem cannot be solved in polynomial time for arbitrary graphs unless P = NP. Stronger hardness results are also known showing that it is difficult to approximate. However, it has a linear time solution for directed acyclic graphs, which has important applications in finding the critical path in scheduling problems.

In graph theory, a branch of mathematics, a **Hamiltonian decomposition** of a given graph is a partition of the edges of the graph into Hamiltonian cycles. Hamiltonian decompositions have been studied both for undirected graphs and for directed graphs; in the undirected case, a Hamiltonian decomposition can also be described as a 2-factorization of the graph such that each factor is connected.

In the mathematical area of graph theory, an undirected graph *G* is **strongly chordal** if it is a chordal graph and every cycle of even length in *G* has an *odd chord*, i.e., an edge that connects two vertices that are an odd distance (>1) apart from each other in the cycle.

In graph theory, the **tree-depth** of a connected undirected graph *G* is a numerical invariant of *G*, the minimum height of a Trémaux tree for a supergraph of *G*. This invariant and its close relatives have gone under many different names in the literature, including vertex ranking number, ordered chromatic number, and minimum elimination tree height; it is also closely related to the cycle rank of directed graphs and the star height of regular languages. Intuitively, where the treewidth graph width parameter measures how far a graph is from being a tree, this parameter measures how far a graph is from being a star.

In graph theory, a **caterpillar** or **caterpillar tree** is a tree in which all the vertices are within distance 1 of a central path.

In graph theory, a branch of mathematics, the *k*th power*G*^{k} of an undirected graph *G* is another graph that has the same set of vertices, but in which two vertices are adjacent when their distance in *G* is at most *k*. Powers of graphs are referred to using terminology similar to that of exponentiation of numbers: *G*^{2} is called the **square** of *G*, *G*^{3} is called the **cube** of *G*, etc.

In the mathematical area of graph theory, a **k-leaf power** of a tree is a graph whose vertices are the leaves of and whose edges connect pairs of leaves whose distance in is at most . That is, is an induced subgraph of the graph power , induced by the leaves of . For a graph constructed in this way, is called a **k-leaf root** of .

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