application: graph
The application graph deals with directed and undirected graphs. They can be defined abstractly as a set of nodes and EDGES or as part of a larger structure for instance as the vertexedge graph of a polytope.
Objects

Category: CombinatoricsUNDOCUMENTEDderived from: Graph<Undirected>
Properties of GeometricGraph
 BOUNDING_BOX: common::Matrix
Since a Voronoi polyhedron is unbounded it must be artificially bounded for visualization purposes. Allowed is any set of hyperplanes which makes the projection onto the last d1 coordinates bounded. By default, these are the vertical facets of a suitably scaled cube.

Category: Combinatorics
A graph with optional node and edge attributes.
Specializations of Graph
 Graph<Undirected>
Unnamed full specialization of Graph
 Graph<Directed>
Unnamed full specialization of Graph
Properties of Graph
 ADJACENCY: common::Graph
combinatorial description of the Graph in the form of adjacency matrix
Example: This prints the adjacency matrix of the complete graph of three nodes, whereby the ith node is connected to the nodes shown in the ith row:
> print complete(3)>ADJACENCY;
{1 2}
{0 2}
{0 1}
 AVERAGE_DEGREE: common::RationalOnly defined for Graph<Undirected>
The average degree of a node
Example: The following prints the average degree of a node for the complete bipartite graph with 1 and 3 nodes:
> print complete_bipartite(1,3)>AVERAGE_DEGREE;
3/2
 BICONNECTED_COMPONENTS: common::IncidenceMatrix<NonSymmetric>Only defined for Graph<Undirected>
Biconnected components. Every row contains nodes from a single component. Articulation nodes may occur in several rows.
Example: The following prints the biconnected components of two triangles having a single common node:
> $g = new Graph<Undirected>(ADJACENCY=>[[1,2],[0,2],[0,1,3,4],[2,4],[2,3]]);
> print $g>BICONNECTED_COMPONENTS;
{2 3 4}
{0 1 2}
 BIPARTITE: common::BoolOnly defined for Graph<Undirected>
True if the graph is a bipartite.
Example: The following checks if a square (as a graph) is bipartite:
> $g = new Graph<Undirected>(ADJACENCY=>[[1,3],[0,2],[1,3],[0,2]]);
> print $g>BIPARTITE;
true
 CHARACTERISTIC_POLYNOMIAL: common::UniPolynomial<Rational, Int>
Characteristic polynomial of the adjacency matrix; its roots are the eigenvalues
Example: The following prints the characteristic polynomial of a complete graph with three nodes:
> print complete(3)>CHARACTERISTIC_POLYNOMIAL
x^3 3*x 2
 CONNECTED: common::Bool
True if the graph is a connected graph.
Example: The following checks whether a graph is connected or not. A single edge with its endpoints is obviously connected. We construct the adjacency matrix beforehand:
> $IM = new IncidenceMatrix<Symmetric>([[1],[0]]);
> print new Graph(ADJACENCY=>$IM)>CONNECTED;
true
The same procedure yields the opposite outcome after adding an isolated node:> $IM = new IncidenceMatrix<Symmetric>([[1],[0],[]]);
> print new Graph(ADJACENCY=>$IM)>CONNECTED;
false
 CONNECTED_COMPONENTS: common::IncidenceMatrix<NonSymmetric>Only defined for Graph<Undirected>
The connected components. Every row contains nodes from a single component.
Example: The following prints the connected components of two seperate triangles:
> $g = new Graph<Undirected>(ADJACENCY=>[[1,2],[0,2],[0,1],[4,5],[3,5],[3,4]]);
> print $g>CONNECTED_COMPONENTS;
{0 1 2}
{3 4 5}
 CONNECTIVITY: common::IntOnly defined for Graph<Undirected>
Node connectivity of the graph, that is, the minimal number of nodes to be removed from the graph such that the result is disconnected.
Example: The following prints the connectivity of the complete bipartite graph of 2 and 4 nodes:
> print complete_bipartite(2,4)>CONNECTIVITY;
2
 DEGREE_SEQUENCE: common::Map<Int, Int>Only defined for Graph<Undirected>
How many times a node of a given degree occurs
Example: The following prints how often each degree of a node in the complete bipartite graph with 1 and 3 nodes occurs, whereby the first entry of a tuple represents the degree:
> print complete_bipartite(1,3)>DEGREE_SEQUENCE;
{(1 3) (3 1)}
 DIAMETER: common::Int
The diameter of the graph.
Example: This prints the diameter of the complete bipartite graph of 1 and 3 nodes:
> print complete_bipartite(1,3)>NODE_DEGREES;
3 1 1 1
 EIGENVALUES_LAPLACIAN: common::Vector<Float>
eigenvalues of the discrete laplacian operator of a graph
Example: The following prints the eigenvalues of the discrete laplacian operator of the complete graph with 3 nodes:
> print complete(3)>EIGENVALUES_LAPLACIAN
3 3 0
 MAX_CLIQUES: common::PowerSet<Int>Only defined for Graph<Undirected>
The maximal cliques of the graph, encoded as node sets.
Example: The following prints the maximal cliques of two complete graphs with 3 nodes being connected by a single edge:
> $g = new Graph<Undirected>(ADJACENCY=>[[1,2],[0,2],[0,1,3],[2,4,5],[3,5],[3,4]]);
> print $g>MAX_CLIQUES;
{{0 1 2} {2 3} {3 4 5}}
 NODE_DEGREES: common::Array<Int>
Degrees of nodes in the graph.
Example: This prints the node degrees of the complete bipartite graph of 1 and 3 nodes:
> print complete_bipartite(1,3)>NODE_DEGREES;
3 1 1 1
 NODE_IN_DEGREES: common::Array<Int>Only defined for Graph<Directed>
The number of incoming edges of the graph nodes.
Example: To print the number of incoming edges of a directed version of the complete graph with 3 nodes, type this:
> $g = new Graph<Directed>(ADJACENCY=>[[1,2],[2],[]]);
> print $g>NODE_IN_DEGREES;
0 1 2
 NODE_LABELS: common::Array<String>
Labels of the nodes of the graph.
Example: The following prints the node labels of the generalized_johnson_graph with parameters (4,2,1):
> print generalized_johnson_graph(4,2,1)>NODE_LABELS;
{0 1} {0 2} {0 3} {1 2} {1 3} {2 3}
 NODE_OUT_DEGREES: common::Array<Int>Only defined for Graph<Directed>
The number of outgoing edges of the graph nodes.
Example: To print the number of incoming edges of a directed version of the complete graph with 3 nodes, type this:
> $g = new Graph<Directed>(ADJACENCY=>[[1,2],[2],[]]);
> print $g>NODE_OUT_DEGREES;
2 1 0
 N_CONNECTED_COMPONENTS: common::IntOnly defined for Graph<Undirected>
Number of CONNECTED_COMPONENTS of the graph.
Example: The following prints the number of connected components of two seperate triangles:
> $g = new Graph<Undirected>(ADJACENCY=>[[1,2],[0,2],[0,1],[4,5],[3,5],[3,4]]);
> print $g>N_CONNECTED_COMPONENTS;
2
 N_EDGES: common::Int
Number of EDGES of the graph.
Example: This prints the number of edges of the complete graph of 4 nodes (which is 4 choose 2):
> print complete(4)>N_EDGES;
6
 N_NODES: common::Int
Number of nodes of the graph.
Example: This prints the number of nodes of the complete bipartite graph of 1 and 3 nodes (which is the sum of the nodes in each partition):
> print complete_bipartite(1,3)>N_NODES;
4
 SIGNATURE: common::IntOnly defined for Graph<Undirected>
Difference of the black and white nodes if the graph is BIPARTITE. Otherwise 1.
Example: The following prints the signature of the complete bipartite graph with 2 and 7 nodes:
> print complete_bipartite(2,7)>SIGNATURE;
5
 SIGNED_INCIDENCE_MATRIX: common::SparseMatrix<Int, NonSymmetric>
Signed vertexedge incidence matrix; for undirected graphs, the orientation comes from the lexicographic order of the nodes.
Example: The following prints the signed incidence matrix of the cylcic graph with 4 nodes:
> print cycle_graph(4)>SIGNED_INCIDENCE_MATRIX;
1 0 1 0
1 1 0 0
0 1 0 1
0 0 1 1
 STRONGLY_CONNECTED: common::BoolOnly defined for Graph<Directed>
True if the graph is strongly connected
Example: The following checks a graph for strong connectivity. First we construct a graph with 4 nodes consisting of two directed circles, which are connected by a single directed edge. We then and check the property:
> $g = new Graph<Directed>(ADJACENCY=>[[1],[0,2],[3],[2]]);
> print $g>STRONGLY_CONNECTED;
false
The same procedure yields the opposite result for the graph with the reversed edge added in the middle:> $g = new Graph<Directed>(ADJACENCY=>[[1],[0,2],[1,3],[2]]);
> print $g>STRONGLY_CONNECTED;
true
 STRONG_COMPONENTS: common::IncidenceMatrix<NonSymmetric>Only defined for Graph<Directed>
Strong components. Every row contains nodes from a single component
Example: To print the strong connected components of a graph with 4 nodes consisting of two directed circles and which are connected by a single directed edge, type this:
> $g = new Graph<Directed>(ADJACENCY=>[[1],[0,2],[3],[2]]);
> print $g>STRONG_COMPONENTS;
{2 3}
{0 1}
 TRIANGLE_FREE: common::BoolOnly defined for Graph<Undirected>
Determine whether the graph has triangles or not.
Example: The following checks if the petersen graph contains triangles:
> print petersen()>TRIANGLE_FREE;
true
 WEAKLY_CONNECTED: common::BoolOnly defined for Graph<Directed>
True if the graph is weakly connected
Example: The following checks a graph for weak connectivity. First we construct a graph with 4 nodes consisting of two directed circles, which are connected by a single directed edge. We then check the property:
> $g = new Graph<Directed>(ADJACENCY=>[[1],[0,2],[3],[2]]);
> print $g>WEAKLY_CONNECTED;
true
 WEAKLY_CONNECTED_COMPONENTS: common::IncidenceMatrix<NonSymmetric>Only defined for Graph<Directed>
Weakly connected components. Every row contains nodes from a single component
Example: To print the weakly connected components of a graph with 4 nodes consisting of two directed circles, which are connected by a single directed edge, type this:
> $g = new Graph<Directed>(ADJACENCY=>[[1],[0,2],[3],[2]]);
> print $g>WEAKLY_CONNECTED_COMPONENTS;
{0 1 2 3}
The same procedure yields the opposite outcome using the same graph without the linking edge between the two circles:> $g = new Graph<Directed>(ADJACENCY=>[[1],[0],[3],[2]]);
> print $g>WEAKLY_CONNECTED_COMPONENTS;
{0 1}
{2 3}
User Methods of Graph
These methods capture combinatorial information of the object. Combinatorial properties only depend on combinatorial data of the object like, e.g., the face lattice.
 EDGES () → Array<Set<Int>>
These methods are for visualization.
 VISUAL () → Visual::Graph
Visualizes the graph. Decorations may include
Coord
, disabling the default spring embedder.Options
Int seed random seed value for the spring embedderoption list: Visual::Graph::decorations Returns
Visual::Graph Example: The following visualizes the petersen graph with default settings:
> petersen()>VISUAL;
The following shows some modified visualization style of the same graph:> petersen()>VISUAL(NodeColor=>"green",NodeThickness=>6,EdgeColor=>"purple",EdgeThickness=>4);
Permutations of Graph
 NodePerm
permutation of nodes

Category: Combinatoricsderived from: Graph<Directed>
A Lattice is a poset where join and meet exist for any two elements. It is realized as a directed graph, such that an arbitrar decoration is attached to each node. It is assumed that this decoration always contains at least a face (a Set<Int>) and a rank (an Int). Lattices occur in two different flavors: Sequential and Nonsequential. They are sequential, if it is clear that all nodes occur sorted according to rank (forwards or backwards). Otherwise they should be declared nonsequential. Sequential Lattices are more efficient in terms of storing them in XML files.
Specializations of Lattice
 Lattice::BasicLattice
A Lattice with a BasicDecoration, which corresponds to the legacy HasseDiagram type
Properties of Lattice
These properties capture combinatorial information of the object. Combinatorial properties only depend on combinatorial data of the object like, e.g., the face lattice.
 BOTTOM_NODE: common::Int
The index of the bottom node
Example: The following prints the bottom node of the face lattice of the 2simplex (triangle):
> print simplex(2)>HASSE_DIAGRAM>BOTTOM_NODE;
0
 DECORATION: common::NodeMap
This is the data associated to each node. The prototype for this is BasicDecoration, which consists of properties face and rank.
Example: The following prints this property of the face lattice of the 2simplex (triangle):
> print simplex(2)>HASSE_DIAGRAM>DECORATION;
({} 0)
({0} 1)
({1} 1)
({2} 1)
({1 2} 2)
({0 2} 2)
({0 1} 2)
({0 1 2} 3)
 DIMS: common::Array<Int>
Kept only for backwards compatibility. Basically encodes the INVERSE_RANK_MAP in FaceLattice objects prior to 3.0.7
 FACES: common::NodeMap<Directed, Set<Int>>
The face of each node, realized as a NodeMap. This property is kept for two reasons: As a convenient way to access only the face part of the decoration (in this case the property is temporary) and for reasons of backwards compatibility.
Example: The following prints the faces of the face lattice of the 2simplex (triangle):
> print simplex(2)>HASSE_DIAGRAM>FACES;
{}
{0}
{1}
{2}
{1 2}
{0 2}
{0 1}
{0 1 2}
 INVERSE_RANK_MAP: InverseRankMap
This property provides an efficient way to enumerate all nodes of a given rank. Internally these are realized differently, depending on whether the Lattice is Sequential or Nonsequential. Both provide the same user methods though.
Example: The following prints this property of the face lattice of the 2simplex (triangle), where the tuples represent the ranges of nodes belonging to a specific rank:
> print simplex(2)>HASSE_DIAGRAM>INVERSE_RANK_MAP;
{(0 (0 0)) (1 (1 3)) (2 (4 6)) (3 (7 7))}
 TOP_NODE: common::Int
The index of the top node
Example: The following prints the top node of the face lattice of the 2simplex (triangle):
> print simplex(2)>HASSE_DIAGRAM>TOP_NODE;
7
User Methods of Lattice
These methods capture combinatorial information of the object. Combinatorial properties only depend on combinatorial data of the object like, e.g., the face lattice.
 dual_faces () → Array<Set<Int> >UNDOCUMENTED
Returns
Array<Set<Int> > For each node, contains the indices of maximal nodes it lies below.Example: The following prints the dual faces of the face lattice of the 2simplex (triangle):
> print simplex(2)>HASSE_DIAGRAM>dual_faces();
{0 1 2}
{1 2}
{0 2}
{0 1}
{0}
{1}
{2}
{}
 nodes_of_rank (r) → List<Int>
 nodes_of_rank_range (r1, r2) → List<Int>
These methods are for visualization.
 VISUAL () → Visual::Lattice
Visualize the Lattice.
Options
Int seed random seed value for the node placementoption list: Visual::Lattice::decorations Returns
Visual::Lattice Example: The following visualizes the face lattice of the 2simplex (triangle) with default settings:
> simplex(2)>HASSE_DIAGRAM>VISUAL;
The following shows some modified visualization style of the same lattice:> simplex(2)>HASSE_DIAGRAM>VISUAL(NodeColor=>"green",EdgeThickness=>2,EdgeColor=>"purple");
 VISUAL_DUAL () → Visual::Lattice
Visualize the dual Lattice. This only produces meaningful results for lattice where the codimension one nodes generate the lattice under intersection.
Options
Int seed random seed value for the node placementoption list: Visual::Lattice::decorations Returns
Visual::Lattice Example: The following visualizes the dual face lattice of the 2simplex (triangle) with default settings:
> simplex(2)>HASSE_DIAGRAM>VISUAL_DUAL;
The following shows some modified visualization style of the same lattice:> simplex(2)>HASSE_DIAGRAM>VISUAL_DUAL(NodeColor=>"green",EdgeThickness=>2,EdgeColor=>"purple");

Category: Visualizationderived from: Visual::Object
Collection of nodes and edges of an abstract graph amended with visual decoration attributes and an optional embedding in 3d.

Category: Visualizationderived from: Visual::Graph
Collection of nodes (representing faces of a face lattice) and edges (representing the inclusion relation) amended with visual decoration attributes and an optional embedding in 2d.
User Functions

graph_from_cycles ()
UNDOCUMENTED

These functions capture combinatorial information of the object. Combinatorial properties only depend on combinatorial data of the object like, e.g., the face lattice.

all_spanningtrees (G) → Array<Set<int>>
Calculate all spanning trees for a connected graph along the lines of
Donald E. Knuth:The Art of Computer ProgrammingVolume 4, Fascicle 4, 2431, 2006, Pearson Education Inc.Example: The following prints all spanning trees of the complete graph with 3 nodes, whereby each line represents a single spanning tree as an edge set:
> print all_spanningtrees(complete(3)>ADJACENCY);
{0 1}
{1 2}
{0 2}

complement_graph (G) → Graph

connectivity (graph) → Int
Compute the CONNECTIVITY of a given graph using the FordFulkerson flow algorithm.
Example: Compute the connectivity of the vertexedge graph of the square:
> print connectivity(cube(2)>GRAPH>ADJACENCY);
2
This means that at least two nodes or edges need to be removed in order for the resulting graph not to be connected anymore.

eigenvalues_laplacian (G) → Vector<Float>
Compute the eigenvalues of the discrete Laplacian of a graph.
Example:> $v = eigenvalues_laplacian(cycle_graph(4));
> print $v;
4 2 2 0

eigenvalues_laplacian (G) → Vector<Float>
Compute the eigenvalues of the discrete Laplacian of a graph.
Example:> $v = eigenvalues_laplacian(cycle_graph(4)>ADJACENCY);
> print $v;
4 2 2 0

find_lattice_permutation (L1, L2, permutation) → Permutation
This takes two lattices and checks whether they are isomorphic, possibly after applying a permutation to the faces. This function only compares faces and ranks of nodes to determine isomorphism
Parameters
Lattice L1 A latticeLattice L2 Another lattice, having the same decoration and sequential typePermutation permutation A permutation to be applied to the faces. If empty, the identity permutation is chosenReturns
Permutation A permutation on the nodes of the graph, if the lattices are isomorphic. Otherwise an exeption is thrown. 
graph_homomorphisms (G, H) → Array<Array<Int>>
Enumerate all homomorphisms (edgepreserving maps) from one graph to another
Parameters
Graph G Graph H Options
Bool allow_loops Should edges of G be allowed to collapse to a loop when mapped to H? Default 0Array<Int> prescribed_map A vector of length G.nodes() with those images in G that should be fixed. Negative entries will be enumerated over.Returns
Array<Array<Int>> 
incidence_matrix (G) → SparseMatrix<Int>
Compute the unsigned vertexedge incidence matrix of the graph.
Example:> $I = incidence_matrix(cycle_graph(4));
> print $I
1 0 1 0
1 1 0 0
0 1 0 1
0 0 1 1

incidence_matrix (G) → SparseMatrix<Int>
Compute the unsigned vertexedge incidence matrix of the graph.
Example:> $I = incidence_matrix(cycle_graph(4)>ADJACENCY);
> print $I;
1 0 1 0
1 1 0 0
0 1 0 1
0 0 1 1

laplacian (G) → SparseMatrix<Rational>
Compute the Laplacian matrix of a graph.
Example:> $I = laplacian(cycle_graph(4));
> print $I;
2 1 0 1
1 2 1 0
0 1 2 1
1 0 1 2

laplacian (G) → SparseMatrix<Rational>
Compute the Laplacian matrix of a graph.
Example:> $I = laplacian(cycle_graph(4)>ADJACENCY);
> print $I;
2 1 0 1
1 2 1 0
0 1 2 1
1 0 1 2

lattice_of_chains (lattice) → Lattice<BasicDecoration>
For a given lattice, this computes the lattice of chains from bottom to top node. The result always includes an artificial top node.
Parameters
Lattice<Decoration> lattice Returns
Lattice<BasicDecoration> Faces are sets of nodes of elements in the original lattice forming a chain, ranks are lenghts of chainsExample: The following prints all faces with their ranks of the lattice of chains of the face lattice of the 0simplex (a single point):
> print lattice_of_chains(simplex(0)>HASSE_DIAGRAM)>DECORATION;
({1} 3)
({0 1} 2)
({0} 1)
({1} 1)
({} 0)

line_graph (G) → Graph

maximal_chains_of_lattice (F) → IncidenceMatrix
Computes the set of maximal chains of a Lattice object.
Parameters
Lattice F Options
Bool ignore_bottom_node If true, the bottom node is not included in the chains. False by defaultBool ignore_top_node If true, the top node is not included in the chains. False by defaultReturns
IncidenceMatrix Each row is a maximal chain, indices refer to nodes of the LatticeExample: The following prints all maximal chains of the face lattice of the 1simplex (an edge):
> print maximal_chains_of_lattice(simplex(1)>HASSE_DIAGRAM);
{0 1 3}
{0 2 3}

n_graph_homomorphisms (G, H) → Int
Count all homomorphisms (edgepreserving maps) from one graph to another. They are in fact enumerated, but only the count is kept track of using constant memory.
Parameters
Graph G Graph H Options
Bool allow_loops Should edges of G be allowed to collapse to a loop when mapped to H? Default 0Array<Int> prescribed_map A vector of length G.nodes() with those images in G that should be fixed. Negative entries will be enumerated over.Returns
Int 
signed_incidence_matrix (G) → SparseMatrix<Int>
Compute the signed vertexedge incidence matrix of the graph. In case of undirected graphs, the orientation of the edges is induced by the order of the nodes.
Example:> $I = signed_incidence_matrix(cycle_graph(4));
> print $I;
1 0 1 0
1 1 0 0
0 1 0 1
0 0 1 1

signed_incidence_matrix (G) → SparseMatrix<Int>
Compute the signed vertexedge incidence matrix of the graph. In case of undirected graphs, the orientation of the edges is induced by the order of the nodes.
Example:> $I = signed_incidence_matrix(cycle_graph(4)>ADJACENCY);
> print $I;
1 0 1 0
1 1 0 0
0 1 0 1
0 0 1 1


These clients are concerned with automorphisms of graphs and determining whether graphs are isomorphic.

automorphisms (graph) → Array<Array<Int>>
Find the automorphism group of the graph.
Example: We first create the vertexedge graph of the square and then print its automorphism group:
> $g=new props::Graph(cube(2)>GRAPH>ADJACENCY);
> print automorphisms($g);
0 2 1 3
1 0 3 2
These two permutations generate the group of all node permutations that preserve vertexedge connectivity.
Depends on: bliss or nauty 
automorphisms (m) → Array<Pair<Array<Int>,Array<Int>>>
Find the automorphism group of the nonsymmetric incidence matrix.
Parameters
IncidenceMatrix<NonSymmetric> m Returns
Array<Pair<Array<Int>,Array<Int>>> each element encodes a permutation of its rows (first
) and columns (second
).Example: The group of combinatorial automorphisms of the 3cube coincides with the group of (bipartite) graph automorphisms of the vertex/facet incidences. To print this group, type this:
> print automorphisms(cube(3)>VERTICES_IN_FACETS);
(<0 1 4 5 2 3> <0 1 4 5 2 3 6 7>)
(<2 3 0 1 4 5> <0 2 1 3 4 6 5 7>)
(<1 0 2 3 4 5> <1 0 3 2 5 4 7 6>)
This means that the group is generated by three elements, one per line in the output. Each is written as a pair of permutations. The first gives the action on the facets, the second the action on the vertices.
Depends on: bliss or nauty 
automorphisms (m) → Array<Array<Int>>
Find the automorphism group of the symmetric incidence matrix.
Parameters
IncidenceMatrix<Symmetric> m Returns
Array<Array<Int>> each element encodes a permutation of its rows (=columns).Depends on: bliss or nauty 
canonical_form (g) → props::Graph
Find a canonical representation of a graph g. Warning: This representation can depend on the extension (bliss/nauty) used, its version and configuration, as well as the hardware!
Depends on: bliss or nauty 
canonical_hash (g, k) → Int
Compute a hash for a graph g independent of the node ordering. Warning: This hash can depend on the extension (bliss/nauty) used, its version and configuration, as well as the hardware! Nauty requires an integer key k as input, bliss will ignore the key.
Depends on: bliss or nauty 
canonical_hash (M, k) → Int
Compute a hash for an incidence matrix I independent of the row ordering. Warning: This hash can depend on the extension (bliss/nauty) used, its version and configuration, as well as the hardware! Nauty requires an integer key k as input, bliss will ignore the key.
Depends on: bliss or nauty 
find_node_permutation (graph1, graph2) → Array<Int>
Find the node permutation mapping graph1 to graph2. If the given graphs are not isomorphic, throw an expection.
Depends on: bliss or nauty 
find_row_col_permutation (m1, m2) → Pair<Array<Int>,Array<Int>>
Find the permutations mapping the nonsymmetric incidence matrix m1 to m2. If the given matrices are not isomorphic, throw an expection.
Parameters
IncidenceMatrix<NonSymmetric> m1 IncidenceMatrix<NonSymmetric> m2 Returns
Pair<Array<Int>,Array<Int>> first
permutation applies to the rows,second
applies to the columnsExample:> $m1 = new IncidenceMatrix([1,2],[5,3]);
> $m2 = new IncidenceMatrix([4,3],[1,5]);
> print find_row_col_permutation($m1,$m2);
<1 0> <0 1 4 3 5 2>
Depends on: bliss or nauty 
isomorphic (IncidenceMatrix1, IncidenceMatrix2) → Bool
true if IncidenceMatrix1 and IncidenceMatrix2 are isomorphic.
Example: Compare the incidence matrices of the 2dimensional cube and cross polytope:
> $I1 = cube(2)>VERTICES_IN_FACETS;
> $I2 = cross(2)>VERTICES_IN_FACETS;
> print isomorphic($I1,$I2);
true
Depends on: bliss or nauty 
isomorphic (graph1, graph2) → Bool
true if graph1 and graph2 are isomorphic.
Example: Compare the vertexedge graph of the square with the cycle graph on 4 nodes:
> $g1 = cube(2)>GRAPH>ADJACENCY;
> $g2 = cycle_graph(4)>ADJACENCY;
> print isomorphic($g1,$g2);
true
Depends on: bliss or nauty 
n_automorphisms (graph) → Int
Find the order of the automorphism group of the graph.
Example:> print n_automorphisms(cycle_graph(5)>ADJACENCY);
2
Depends on: bliss or nauty


Special purpose functions.

edge_lengths (G, coords) → EdgeMap
Compute the lengths of all edges of a given graph G from the coordinates coords of its nodes.
Parameters
props::Graph<Directed> G the input graphMatrix coords the coordinates of the nodesReturns
EdgeMap Example: The following prints the edge length of the complete graph with 3 nodes and edge lengths given by the coordiantes of the standard 2simplex:
> print edge_lengths(complete(3)>ADJACENCY,simplex(2)>VERTICES);
1 1 1.414213562

graph_from_edges (edges) → Graph
Creates a graph from a given list of edges.
Example:> $g = graph_from_edges([[1,2],[1,3],[1,4]]);
> print $g>ADJACENCY;
{}
{2 3 4}
{1}
{1}
{1}


With these clients you can create special examples of graphs, graphs belonging to parameterized families and random graphs.

complete_bipartite (k, l) → Graph

cycle_graph (n) → Graph

generalized_johnson_graph (n, k, i) → Graph
Create the generalized Johnson graph on parameters (n,k,i). It has one node for each set in \({[n]}\choose{k}\), and an edge between two nodes iff the intersection of the corresponding subsets is of size i.
Parameters
Int n the size of the ground setInt k the size of the subsetsInt i the size of the subsetsReturns
Graph Example: The following prints the adjacency representation of the generalized johnson graph with the parameters 4,2,1:
> print generalized_johnson_graph(4,2,1)>ADJACENCY;
{1 2 3 4}
{0 2 3 5}
{0 1 4 5}
{0 1 4 5}
{0 2 3 5}
{1 2 3 4}

johnson_graph (n, k) → Graph
Create the Johnson graph on parameters (n,k). It has one node for each set in \({[n]}\choose{k}\), and an edge between two nodes iff the intersection of the corresponding subsets is of size k1.
Example: The following prints the adjacency representation of the johnson graph with the parameters 4,3:
> print johnson_graph(4,3)>ADJACENCY;
{1 2 3}
{0 2 3}
{0 1 3}
{0 1 2}

kneser_graph (n, k) → Graph
Create the Kneser graph on parameters (n,k). It has one node for each set in \({[n]}\choose{k}\), and an edge between two nodes iff the corresponding subsets are disjoint.
Example: The following prints the adjacency representation of the kneser graph with the parameters 3,1:
> print kneser_graph(3,1)>ADJACENCY;
{1 2}
{0 2}
{0 1}

neighborhood_graph (D, delta) → Graph
Constructs the neighborhood graph of a point set S given a parameter delta. The set is passed as its socalled "distance matrix", whose (i,j)entry is the distance between point i and j. This matrix can e.g. be computed using the distance_matrix function. Two vertices will be adjacent if the distance of the corresponding points is less than delta.
Parameters
Matrix<Rational> D input point cloud distance matrix (can be upper triangular)Rational delta the maximal distance of neighbored verticesReturns
Graph Example: The following prints the neighborhood graph of a distance matrix with a limit of 3.3, producing the graph of a square:
> $D = new Matrix<Rational>([[0,17/10,21/10,42/10],[0,0,79/10,31/10],[0,0,0,6/10],[0,0,0,0]]);
> print neighborhood_graph($D,3.3)>ADJACENCY;
{1 2}
{0 3}
{0 3}
{1 2}

path_graph (n) → Graph

random_graph (n) → Graph
Constructs a random graph with n nodes according to the ErdosRenyi model. Each edge is chosen uniformly with probability p.
Parameters
Int n Options
Rational p the probability of an edge occurring; default 1/2Bool try_connected whether to try to generate a connected graph, default 1Int max_attempts If connected is set, specifies how many times to try to make a connected random graph before giving up.Int seed controls the outcome of the random number generator; fixing a seed number guarantees the same outcome.Returns
Graph Example: The following produces a connected graph on 10 nodes using a specific seed for a random graph model, where an edge between two nodes occurs with probabilty 0.1.
> $g = random_graph(10,p=>0.1,try_connected=>1,max_attempts=>50,seed=>100000);
> print $g>N_EDGES;
9

These functions are for visualization.

clip_graph (G, V, BB) → GeometricGraph
Clip a graph with respect to a given bounding box. Used for the visualization of Voronoi diagrams.

graphviz (vis_obj ...)
Draw the given graph or face lattice object using graphviz program
neato
ordot
respectively. The output is rendered in PostScript format and fed into a viewer program, if one is configured. If you prefer to produce another output format, please use the File option and call theneato
ordot
program manually.Parameters
Visual::Object vis_obj ... objects to displayOptions
String File "filename" or "AUTO" Store the graph description in a DOT source file without starting the interactive GUI. The.dot
suffix is automatically added to the file name.Specify AUTO if you want the filename be automatically derived from the drawing title.You can also use any expression allowed for theopen
function, including "" for terminal output, "&HANDLE" for an already opened file handle, or " program" for a pipe.Example: The following creates a star graph with 4 nodes and visualizes it via graphviz with default options:
> $g = new Graph<Undirected>(ADJACENCY=>[[],[0],[0],[0]]);
> graphviz($g>VISUAL);
The following shows some modified visualization style of the same graph:> $g = new Graph<Undirected>(ADJACENCY=>[[],[0],[0],[0]]);
> graphviz($g>VISUAL(NodeColor=>"green",EdgeColor=>"purple",EdgeThickness=>5));

hd_embedder (label_width)
Create an embedding of the Lattice as a layered graph. The embedding algorithm tries to minimize the weighted sum of squares of edge lengths, starting from a random distribution. The weights are relative to the fatness of the layers. The yspace between the layers is constant.
Parameters
Array label_width estimates (better upper bounds) of the label width of each node. The computed layout guarantees that the distances between the nodes in a layer are at least equal to the widest label in this layer.Options
Bool dual the node representing the empty face is put on the topmost levelFloat eps calculation accuracy.Int seed effects the initial placement of the nodes. 
LEDA_graph (G)

metapost (vis_obj ...)
Produce a MetaPost input file with given visual objects.
Parameters
Visual::Object vis_obj ... objects to displayOptions
String File "filename" or "AUTO" The MetaPost description always has to be stored in a file, there is no interactive viewer for this kind of visualization.For the file name you can use any expression allowed for theopen
function, including "" for terminal output, "&HANDLE" for an already opened file handle, or " program" for a pipe. Real file names are automatically completed with the.mp
suffix if needed.The default setting "AUTO" lets the file name be derived from the drawing title. The automatically generated file name is displayed in the verbose mode.Example: The following prints a metapost description of the complete graph with 3 nodes in the console:
> metapost(complete(3)>VISUAL,File=>"");

spring_embedder (graph)
Produce a 3d embedding for the graph using the spring embedding algorithm along the lines of
Thomas Fruchtermann and Edward Reingold:Graph Drawing by Forcedirected Placement.Software Practice and Experience Vol. 21, 11291164 (1992), no. 11.Parameters
props::Graph<Undirected> graph to be embedded.Options
affecting the desired pictureEdgeMap edge_weights relative edge lengths. By default the embedding algorithm tries to stretch all edges to the same length.Vector zordering an objective function provides an additional force along the zaxis, trying to rearrange nodes in the order of the function growth.Float zfactor gain coefficient applied to the zordering force.Int seed random seed for initial node placement on a unit sphere.calculation finetuningFloat scale enlarges the ideal edge lengthFloat balance changes the balance between the edge contraction and node repulsion forcesFloat inertion affects how the nodes are moved, can be used to restrain oscillationsFloat viscosity idemFloat eps a threshold for point movement between iterations, below that it is considered to stand stillInt maxiterations hard limit for computational efforts. The algorithm terminates at latest after that many iterations regardless of the convergence achieved so far.Example: The following prints a 3dimensional embedding of the complete graph on 3 nodes using a specific seed and scaled edge lengths:
> print spring_embedder(complete(3)>ADJACENCY, scale=>5, seed=>123);
0.9512273649 10.00210559 10.36309695
10.61947526 1.391783824 9.666627553
11.57070263 8.610321763 0.6964693941

Property Types

These property_types capture combinatorial information of the object. Combinatorial properties only depend on combinatorial data of the object like, e.g., the face lattice.

BasicDecoration
This is the prototype of decorations attached to a Lattice. It consists of two properties, face and rank of type Set<Int> and Int, respectively.

InverseRankMap
This provides an efficient way to obtain all nodes of a lattice of given rank.
User Methods of InverseRankMap

These methods capture combinatorial information of the object. Combinatorial properties only depend on combinatorial data of the object like, e.g., the face lattice.

get_map () → Map<Int, List<Int> >UNDOCUMENTED
Returns
Map<Int, List<Int> > or Map<Int, Pair<Int,Int> >. An actual map object sorting nodes according to rank. In the nonsequential case, each integer (= rank) is mapped to a list of the corresponding nodes.\ In the sequential case, it is mapped to the first and last index of all nodes of that rank. 
nodes_of_rank (r) → List<Int>

nodes_of_rank_range (r1, r2) → List<Int>



Nonsequential
Nonsequential lattices are those where not all nodes are necessarily sorted to rank.

Sequential
Sequential lattices are those where all nodes are sorted to rank.

Common Option Lists

These options are for visualization.

Visual::Graph::decorationsimports from: Visual::Wire::decorations, Visual::PointSet::decorations
Attributes modifying the appearance of graphs
Options
Matrix<Float> Coord 2d or 3d coordinates of the nodes. If not specified, a random embedding is generated using a pseudophysical spring modelFlexible<RGB> NodeColor alias for PointColorFlexible<Float> NodeThickness alias for PointThicknessFlexible<RGB> NodeBorderColor alias for PointBorderColorFlexible<Float> NodeBorderThickness alias for PointBorderThicknessFlexible<String> NodeStyle alias for PointStyleString NodeLabels alias for PointLabelsFlexible<Int> ArrowStyle How to draw directed edges: 0 (like undirected), 1 (with an arrow pointing towards the edge), or 1 (with an arrow pointing against the edge). Default is 1 for directed graphs and lattices. 
Visual::Lattice::decorationsimports from: Visual::Graph::decorations, Visual::Wire::decorations, Visual::PointSet::decorations
Attributes modifying the appearance of face lattices
Options
Array<String> AtomLabels Labels of atoms, to use as building blocks for node labels. By default the ordinal numbers are taken.
