Classes
struct	BreadthFirstSearch

struct	DepthFirstSearch

class	DisjointSetUnionFind
	Disjoint Set Union-Find data structure. More...

struct	PartitioningOptions
	Options for graph partitioning. More...

struct	WeightedEdgeTraits
	Traits for WeightedEdge. More...

Typedefs
template<class TWeight = Scalar, class TIndex = Index>
using	WeightedEdge = Eigen::Triplet<TWeight, TIndex>
	Weighted edge (wrapper around Eigen triplet type)

Enumerations
enum class	EGreedyColorSelectionStrategy { LeastUsed , FirstAvailable }
	Enumeration of color selection strategies for graph coloring algorithms. More...

enum class	EGreedyColorOrderingStrategy { Natural , SmallestDegree , LargestDegree }
	Enumeration of vertex traversal ordering strategies for graph coloring algorithms. More...

enum class	EMeshDualGraphOptions : std::int32_t { VertexAdjacent = 0b001 , EdgeAdjacent = 0b010 , FaceAdjacent = 0b100 , All = 0b111 }
	Types of dual graph adjacencies.

Functions
template<class TWeightedEdgeIterator, class TWeightedEdge = typename std::iterator_traits<TWeightedEdgeIterator>::value_type, class TScalar = typename WeightedEdgeTraits<TWeightedEdge>::ScalarType, class TIndex = typename WeightedEdgeTraits<TWeightedEdge>::IndexType>
auto	AdjacencyMatrixFromEdges (TWeightedEdgeIterator begin, TWeightedEdgeIterator end, TIndex m=TIndex(-1), TIndex n=TIndex(-1)) -> Eigen::SparseMatrix< TScalar, Eigen::RowMajor, TIndex >
	Construct adjacency matrix from edge/triplet list.

template<class TDerivedA, class TScalar = typename TDerivedA::Scalar, std::integral TIndex = typename TDerivedA::StorageIndex>
auto	AdjacencyMatrixPrefix (Eigen::SparseCompressedBase< TDerivedA > const &A)
	Non-owning wrapper around the offset pointers of a compressed sparse matrix.

template<class TDerivedA, class TScalar = typename TDerivedA::Scalar, std::integral TIndex = typename TDerivedA::StorageIndex>
auto	AdjacencyMatrixIndices (Eigen::SparseCompressedBase< TDerivedA > const &A)
	Non-owning wrapper around the indices of a compressed sparse matrix.

template<class TDerivedA, class TScalar = typename TDerivedA::Scalar, std::integral TIndex = typename TDerivedA::StorageIndex>
auto	AdjacencyMatrixWeights (Eigen::SparseCompressedBase< TDerivedA > const &A)
	Non-owning wrapper around the weights of a compressed sparse matrix.

template<class TDerivedP, std::integral TIndex = typename TDerivedP::Scalar>
auto	MapToAdjacency (Eigen::DenseBase< TDerivedP > const &p, TIndex n=TIndex(-1)) -> std::tuple< Eigen::Vector< TIndex, Eigen::Dynamic >, Eigen::Vector< TIndex, Eigen::Dynamic > >
	Construct adjacency list in compressed sparse format from a map p s.t. p(i) is the index of the vertex adjacent to i.

template<class TDerivedA, class TScalar = typename TDerivedA::Scalar, std::integral TIndex = typename TDerivedA::StorageIndex>
auto	MatrixToAdjacency (Eigen::SparseCompressedBase< TDerivedA > const &A)
	Obtain the offset and indices arrays of an input adjacency matrix.

template<class TDerivedA, class TScalar = typename TDerivedA::Scalar, std::integral TIndex = typename TDerivedA::StorageIndex>
auto	MatrixToWeightedAdjacency (Eigen::SparseCompressedBase< TDerivedA > const &A)
	Construct adjacency list in compressed sparse format from an input adjacency matrix.

template<class TIndex = Index>
auto	ListOfListsToAdjacency (std::vector< std::vector< TIndex > > const &lil)
	Construct adjacency list in compressed sparse format from an input adjacency list in list of lists format.

template<class TDerivedPtr, class TDerivedAdj, class TIndex = typename TDerivedPtr::Scalar, class Func>
void	ForEachEdge (Eigen::DenseBase< TDerivedPtr > &ptr, Eigen::DenseBase< TDerivedAdj > &adj, Func &&f)
	Edge iteration over the adjacency list in compressed sparse format.

template<class TDerivedPtr, class TDerivedAdj, class TIndex = typename TDerivedPtr::Scalar, class Func>
void	RemoveEdges (Eigen::DenseBase< TDerivedPtr > &ptr, Eigen::DenseBase< TDerivedAdj > &adj, Func fShouldDeleteEdge)
	In-place removal of edges from the adjacency list in compressed sparse format.

template<class TDerivedPtr, class TDerivedAdj, int NC = 128, std::integral TIndex = typename TDerivedPtr::Scalar>
auto	GreedyColor (Eigen::DenseBase< TDerivedPtr > const &ptr, Eigen::DenseBase< TDerivedAdj > const &adj, EGreedyColorOrderingStrategy eOrderingStrategy=EGreedyColorOrderingStrategy::LargestDegree, EGreedyColorSelectionStrategy eSelectionStrategy=EGreedyColorSelectionStrategy::LeastUsed) -> Eigen::Vector< TIndex, Eigen::Dynamic >
	Greedy graph coloring algorithm.

template<common::CIndex TIndex, class TDerivedP, class TDerivedAdj>
TIndex	ConnectedComponents (Eigen::DenseBase< TDerivedP > const &ptr, Eigen::DenseBase< TDerivedAdj > const &adj, Eigen::Ref< Eigen::Vector< TIndex, Eigen::Dynamic > > components, DepthFirstSearch< TIndex > &dfs)
	Compute connected components of a graph using depth-first search.

template<common::CIndex TIndex, class TDerivedP, class TDerivedAdj>
TIndex	ConnectedComponents (Eigen::DenseBase< TDerivedP > const &ptr, Eigen::DenseBase< TDerivedAdj > const &adj, Eigen::Ref< Eigen::Vector< TIndex, Eigen::Dynamic > > components, BreadthFirstSearch< TIndex > &bfs)
	Compute connected components of a graph using breadth-first search.

template<class TDerivedE, class TDerivedW, common::CIndex TIndex = typename TDerivedE::Scalar, common::CArithmetic TScalar = typename TDerivedW::Scalar>
auto	MeshAdjacencyMatrix (Eigen::DenseBase< TDerivedE > const &E, Eigen::DenseBase< TDerivedW > const &w, TIndex nNodes=TIndex(-1), bool bVertexToElement=false, bool bHasDuplicates=false) -> Eigen::SparseMatrix< TScalar, Eigen::ColMajor, TIndex >
	Construct adjacency matrix from mesh.

template<class TDerivedE, common::CIndex TIndex = typename TDerivedE::Scalar>
auto	MeshAdjacencyMatrix (Eigen::DenseBase< TDerivedE > const &E, TIndex nNodes=TIndex(-1), bool bVertexToElement=false, bool bHasDuplicates=false) -> Eigen::SparseMatrix< TIndex, Eigen::ColMajor, TIndex >
	Construct adjacency matrix from mesh.

template<class TDerivedE, common::CIndex TIndex = typename TDerivedE::Scalar>
auto	MeshPrimalGraph (Eigen::DenseBase< TDerivedE > const &E, TIndex nNodes=TIndex(-1)) -> Eigen::SparseMatrix< TIndex, Eigen::ColMajor, TIndex >
	Construct primal graph of input mesh, i.e. the graph of adjacent vertices.

template<class TDerivedE, common::CIndex TIndex = typename TDerivedE::Scalar>
auto	MeshDualGraph (Eigen::DenseBase< TDerivedE > const &E, TIndex nNodes=TIndex(-1), EMeshDualGraphOptions opts=EMeshDualGraphOptions::All) -> Eigen::SparseMatrix< TIndex, Eigen::ColMajor, TIndex >
	Construct dual graph of input mesh, i.e. the graph of adjacent elements.

IndexVectorX	Partition (Eigen::Ref< IndexVectorX const > const &ptr, Eigen::Ref< IndexVectorX const > const &adj, Eigen::Ref< IndexVectorX const > const &wadj, Index nPartitions, PartitioningOptions opts=PartitioningOptions{})
	Partition input graph.

Detailed Description

Graph algorithms and data structures

Typedef Documentation

◆ WeightedEdge

template<class TWeight = Scalar, class TIndex = Index>

using pbat::graph::WeightedEdge = Eigen::Triplet<TWeight, TIndex>

Weighted edge (wrapper around Eigen triplet type)

Template Parameters

TWeight	Scalar type of the graph edge weights
TIndex	Index type of the graph vertices

Enumeration Type Documentation

◆ EGreedyColorOrderingStrategy

enum class pbat::graph::EGreedyColorOrderingStrategy

strong

Enumeration of vertex traversal ordering strategies for graph coloring algorithms.

Enumerator
Natural	Natural ordering of the vertices (i.e. [0,n-1])
SmallestDegree	Always visit the vertex with the smallest degree next.
LargestDegree	Always visit the vertex with the largest degree next.

◆ EGreedyColorSelectionStrategy

enum class pbat::graph::EGreedyColorSelectionStrategy

strong

Enumeration of color selection strategies for graph coloring algorithms.

Enumerator
LeastUsed	Select the least used color from the color palette.
FirstAvailable	Select the first available color from the color palette.

Function Documentation

◆ AdjacencyMatrixFromEdges()

template<class TWeightedEdgeIterator, class TWeightedEdge = typename std::iterator_traits<TWeightedEdgeIterator>::value_type, class TScalar = typename WeightedEdgeTraits<TWeightedEdge>::ScalarType, class TIndex = typename WeightedEdgeTraits<TWeightedEdge>::IndexType>

auto pbat::graph::AdjacencyMatrixFromEdges	(	TWeightedEdgeIterator	begin,
		TWeightedEdgeIterator	end,
		TIndex	m = TIndex(-1),
		TIndex	n = TIndex(-1) ) -> Eigen::SparseMatrix<TScalar, Eigen::RowMajor, TIndex>

Construct adjacency matrix from edge/triplet list.

Template Parameters

TWeightedEdgeIterator	Iterator type of the edge list
TWeightedEdge	Type of the edge
TScalar	Scalar type of the graph edge weights
TIndex	Index type of the graph vertices

Parameters

begin	Iterator to the beginning of the edge list
end	Iterator to the end of the edge list
m	Number of rows of the adjacency matrix. If not provided (i.e. m < 0), it is inferred from the edge list.
n	Number of columns of the adjacency matrix. If not provided (i.e. n < 0), it is inferred from the edge list.

Returns: Adjacency matrix

◆ AdjacencyMatrixIndices()

template<class TDerivedA, class TScalar = typename TDerivedA::Scalar, std::integral TIndex = typename TDerivedA::StorageIndex>

auto pbat::graph::AdjacencyMatrixIndices ( Eigen::SparseCompressedBase< TDerivedA > const & A )

Non-owning wrapper around the indices of a compressed sparse matrix.

Template Parameters

TDerivedA	Type of input adjacency matrix
TScalar	Scalar type of the graph edge weights
TIndex	Index type of the graph vertices

Parameters

A	Input adjacency matrix

Returns: Non-owning wrapper around the indices of the adjacency matrix

◆ AdjacencyMatrixPrefix()

template<class TDerivedA, class TScalar = typename TDerivedA::Scalar, std::integral TIndex = typename TDerivedA::StorageIndex>

auto pbat::graph::AdjacencyMatrixPrefix ( Eigen::SparseCompressedBase< TDerivedA > const & A )

Non-owning wrapper around the offset pointers of a compressed sparse matrix.

Template Parameters

TDerivedA	Type of input adjacency matrix
TScalar	Scalar type of the graph edge weights
TIndex	Index type of the graph vertices

Parameters

A	Input adjacency matrix

Returns: Non-owning wrapper around the offset pointers of the adjacency matrix

◆ AdjacencyMatrixWeights()

template<class TDerivedA, class TScalar = typename TDerivedA::Scalar, std::integral TIndex = typename TDerivedA::StorageIndex>

auto pbat::graph::AdjacencyMatrixWeights ( Eigen::SparseCompressedBase< TDerivedA > const & A )

Non-owning wrapper around the weights of a compressed sparse matrix.

Template Parameters

TDerivedA	Type of input adjacency matrix
TScalar	Scalar type of the graph edge weights
TIndex	Index type of the graph vertices

Parameters

A	Input adjacency matrix

Returns: Non-owning wrapper around the weights of the adjacency matrix

◆ ConnectedComponents() [1/2]

template<common::CIndex TIndex, class TDerivedP, class TDerivedAdj>

TIndex pbat::graph::ConnectedComponents	(	Eigen::DenseBase< TDerivedP > const &	ptr,
		Eigen::DenseBase< TDerivedAdj > const &	adj,
		Eigen::Ref< Eigen::Vector< TIndex, Eigen::Dynamic > >	components,
		BreadthFirstSearch< TIndex > &	bfs )

Compute connected components of a graph using breadth-first search.

Template Parameters

TIndex	Index type used in the graph
TDerivedP	Type of the pointer vector (adjacency list start indices for each vertex)
TDerivedAdj	Type of the adjacency list (vector of vertex indices)

Parameters

ptr	Pointer to the start of each vertex's adjacency list
adj	Adjacency list of the graph
components	Output vector to store component labels for each vertex
bfs	Breadth-first search object

Returns: Number of connected components found in the graph

Precondition: components[u] < 0 for all vertices u in the graph

◆ ConnectedComponents() [2/2]

template<common::CIndex TIndex, class TDerivedP, class TDerivedAdj>

TIndex pbat::graph::ConnectedComponents	(	Eigen::DenseBase< TDerivedP > const &	ptr,
		Eigen::DenseBase< TDerivedAdj > const &	adj,
		Eigen::Ref< Eigen::Vector< TIndex, Eigen::Dynamic > >	components,
		DepthFirstSearch< TIndex > &	dfs )

Compute connected components of a graph using depth-first search.

Template Parameters

TIndex	Index type used in the graph
TDerivedP	Type of the pointer vector (adjacency list start indices for each vertex)
TDerivedAdj	Type of the adjacency list (vector of vertex indices)

Parameters

ptr	Pointer to the start of each vertex's adjacency list
adj	Adjacency list of the graph
components	Output vector to store component labels for each vertex
dfs	Depth-first search object

Returns: Number of connected components found in the graph

Precondition: components[u] < 0 for all vertices u in the graph

◆ ForEachEdge()

template<class TDerivedPtr, class TDerivedAdj, class TIndex = typename TDerivedPtr::Scalar, class Func>

void pbat::graph::ForEachEdge	(	Eigen::DenseBase< TDerivedPtr > &	ptr,
		Eigen::DenseBase< TDerivedAdj > &	adj,
		Func &&	f )

Edge iteration over the adjacency list in compressed sparse format.

Template Parameters

TDerivedPtr	Eigen dense expression of the offset pointers of the adjacency list
TDerivedAdj	Eigen dense expression of the indices of the adjacency list
TIndex	Index type of the graph vertices
Func	Callable type of the edge iteration function

Parameters

ptr	Offset pointers of the adjacency list
adj	Indices of the adjacency list
f	Callable function to be applied to each edge with signature `void(TIndex i, TIndex j, TIndex k)`

◆ GreedyColor()

template<class TDerivedPtr, class TDerivedAdj, int NC = 128, std::integral TIndex = typename TDerivedPtr::Scalar>

auto pbat::graph::GreedyColor	(	Eigen::DenseBase< TDerivedPtr > const &	ptr,
		Eigen::DenseBase< TDerivedAdj > const &	adj,
		EGreedyColorOrderingStrategy	eOrderingStrategy = EGreedyColorOrderingStrategy::LargestDegree,
		EGreedyColorSelectionStrategy	eSelectionStrategy = EGreedyColorSelectionStrategy::LeastUsed ) -> Eigen::Vector<TIndex, Eigen::Dynamic>

Greedy graph coloring algorithm.

Template Parameters

TDerivedPtr	Eigen dense expression for offset pointers of the adjacency list
TDerivedAdj	Eigen dense expression for indices of the adjacency list
NC	Color palette capacity
TIndex	Index type for vertices

Parameters

ptr	Offset pointers array of the adjacency list
adj	Indices array of the adjacency list
eOrderingStrategy	Vertex visiting order strategy
eSelectionStrategy	Color selection strategy

Returns: |# vertices| array mapping vertices to their associated color

◆ ListOfListsToAdjacency()

template<class TIndex = Index>

auto pbat::graph::ListOfListsToAdjacency ( std::vector< std::vector< TIndex > > const & lil )

Construct adjacency list in compressed sparse format from an input adjacency list in list of lists format.

Template Parameters

TIndex Index type of the graph vertices

Parameters

lil	Input adjacency list in list of lists format

Returns: Tuple of the offset pointers and indices of the adjacency list

◆ MapToAdjacency()

template<class TDerivedP, std::integral TIndex = typename TDerivedP::Scalar>

auto pbat::graph::MapToAdjacency	(	Eigen::DenseBase< TDerivedP > const &	p,
		TIndex	n = TIndex(-1) ) -> std::tuple<Eigen::Vector<TIndex, Eigen::Dynamic>, Eigen::Vector<TIndex, Eigen::Dynamic>>

Construct adjacency list in compressed sparse format from a map p s.t. p(i) is the index of the vertex adjacent to i.

Template Parameters

TDerivedP	Type of input map
TScalar	Scalar type of the graph edge weights

Parameters

p	Input map
n	Number of vertices in the graph. If not provided (i.e. n < 0), it is inferred from the map.

Returns: Tuple of the offset pointers and indices of the adjacency list

◆ MatrixToAdjacency()

template<class TDerivedA, class TScalar = typename TDerivedA::Scalar, std::integral TIndex = typename TDerivedA::StorageIndex>

auto pbat::graph::MatrixToAdjacency ( Eigen::SparseCompressedBase< TDerivedA > const & A )

Obtain the offset and indices arrays of an input adjacency matrix.

Template Parameters

TDerivedA	Type of input adjacency matrix
TScalar	Scalar type of the graph edge weights
TIndex	Index type of the graph vertices

Parameters

A	Input adjacency matrix

Returns: Tuple of the offset pointers and indices of the adjacency matrix

◆ MatrixToWeightedAdjacency()

template<class TDerivedA, class TScalar = typename TDerivedA::Scalar, std::integral TIndex = typename TDerivedA::StorageIndex>

auto pbat::graph::MatrixToWeightedAdjacency ( Eigen::SparseCompressedBase< TDerivedA > const & A )

Construct adjacency list in compressed sparse format from an input adjacency matrix.

Template Parameters

TDerivedA	Type of input adjacency matrix
TScalar	Scalar type of the graph edge weights
TIndex	Index type of the graph vertices

Parameters

A	Input adjacency matrix

Returns: Tuple of the offset pointers, indices, and weights of the adjacency matrix

◆ MeshAdjacencyMatrix() [1/2]

template<class TDerivedE, class TDerivedW, common::CIndex TIndex = typename TDerivedE::Scalar, common::CArithmetic TScalar = typename TDerivedW::Scalar>

auto pbat::graph::MeshAdjacencyMatrix	(	Eigen::DenseBase< TDerivedE > const &	E,
		Eigen::DenseBase< TDerivedW > const &	w,
		TIndex	nNodes = TIndex(-1),
		bool	bVertexToElement = false,
		bool	bHasDuplicates = false ) -> Eigen::SparseMatrix<TScalar, Eigen::ColMajor, TIndex>

Construct adjacency matrix from mesh.

Template Parameters

TDerivedE	Eigen dense expression for mesh elements
TDerivedW	Eigen dense expression for element-vertex weights
TIndex	Type of indices used in element array
TScalar	Type of weights

Parameters

E	`\|# nodes per element\|x\|# elements\|` array of element indices
w	`\|# nodes per element\|x\|# elements\|` array of element-vertex weights
nNodes	Number of nodes in the mesh. If `nNodes < 1`, the number of nodes is inferred from E.
bVertexToElement	If true, the adjacency matrix maps vertices to elements, rather than elements to vertices
bHasDuplicates	If true, duplicate entries in the input mesh will be handled

Returns: Adjacency matrix of requested mesh connectivity

Precondition: E.rows() == w.rows() and E.cols() == w.cols()

◆ MeshAdjacencyMatrix() [2/2]

template<class TDerivedE, common::CIndex TIndex = typename TDerivedE::Scalar>

auto pbat::graph::MeshAdjacencyMatrix	(	Eigen::DenseBase< TDerivedE > const &	E,
		TIndex	nNodes = TIndex(-1),
		bool	bVertexToElement = false,
		bool	bHasDuplicates = false ) -> Eigen::SparseMatrix<TIndex, Eigen::ColMajor, TIndex>

Construct adjacency matrix from mesh.

Template Parameters

TDerivedE	Eigen dense expression for mesh elements
TIndex	Type of indices used in element array

Parameters

E	`\|# nodes per element\|x\|# elements\|` array of element indices
nNodes	Number of nodes in the mesh. If `nNodes < 1`, the number of nodes is inferred from E.
bVertexToElement	If true, the adjacency matrix maps vertices to elements, rather than
bHasDuplicates	If true, duplicate entries in the input mesh will be handled

Returns: Adjacency matrix of requested mesh connectivity

◆ MeshDualGraph()

template<class TDerivedE, common::CIndex TIndex = typename TDerivedE::Scalar>

auto pbat::graph::MeshDualGraph	(	Eigen::DenseBase< TDerivedE > const &	E,
		TIndex	nNodes = TIndex(-1),
		EMeshDualGraphOptions	opts = EMeshDualGraphOptions::All ) -> Eigen::SparseMatrix<TIndex, Eigen::ColMajor, TIndex>

Construct dual graph of input mesh, i.e. the graph of adjacent elements.

Template Parameters

TDerivedE	Eigen dense expression for mesh elements
TIndex	Type of indices used in element array

Parameters

E	`\|# nodes per element\|x\|# elements\|` array of element indices
nNodes	Number of nodes in the mesh. If `nNodes < 1`, the number of nodes is inferred from E.
opts	Adjacency types to keep in the dual graph

Returns: Dual graph of the input mesh

◆ MeshPrimalGraph()

template<class TDerivedE, common::CIndex TIndex = typename TDerivedE::Scalar>

auto pbat::graph::MeshPrimalGraph	(	Eigen::DenseBase< TDerivedE > const &	E,
		TIndex	nNodes = TIndex(-1) ) -> Eigen::SparseMatrix<TIndex, Eigen::ColMajor, TIndex>

Construct primal graph of input mesh, i.e. the graph of adjacent vertices.

Template Parameters

TDerivedE	Eigen dense expression for mesh elements
TIndex	Type of indices used in element array

Parameters

E	`\|# nodes per element\|x\|# elements\|` array of element indices
nNodes	Number of nodes in the mesh. If `nNodes < 1`, the number of nodes is inferred from E.

Returns: Primal graph of the input mesh

◆ Partition()

PBAT_API IndexVectorX pbat::graph::Partition	(	Eigen::Ref< IndexVectorX const > const &	ptr,
		Eigen::Ref< IndexVectorX const > const &	adj,
		Eigen::Ref< IndexVectorX const > const &	wadj,
		Index	nPartitions,
		PartitioningOptions	opts = PartitioningOptions{} )

Partition input graph.

Internally delegates to METIS [7]

Parameters

ptr	Offset pointers of adjacency list
adj	Indices of adjacency list
wadj	Edge weights of adjacency list
nPartitions	Number of desired partitions
opts	Partitioning options

Returns: |# vertices| array p of partition indices (i.e. p[i] = partition of vertex i)

◆ RemoveEdges()

template<class TDerivedPtr, class TDerivedAdj, class TIndex = typename TDerivedPtr::Scalar, class Func>

void pbat::graph::RemoveEdges	(	Eigen::DenseBase< TDerivedPtr > &	ptr,
		Eigen::DenseBase< TDerivedAdj > &	adj,
		Func	fShouldDeleteEdge )

In-place removal of edges from the adjacency list in compressed sparse format.

Template Parameters

TDerivedPtr	Eigen dense expression of the offset pointers of the adjacency list
TDerivedAdj	Eigen dense expression of the indices of the adjacency list
TIndex	Index type of the graph vertices
Func	Callable type of the edge removal function with signature `bool(TIndex i, TIndex j)`

Parameters

ptr	Offset pointers of the adjacency list
adj	Indices of the adjacency list
fShouldDeleteEdge	Callable function to determine if an edge should be removed with signature `bool(TIndex i, TIndex j)`

Classes

Typedefs

Enumerations

Functions

Detailed Description

Typedef Documentation

◆ WeightedEdge

Enumeration Type Documentation

◆ EGreedyColorOrderingStrategy

◆ EGreedyColorSelectionStrategy

Function Documentation

◆ AdjacencyMatrixFromEdges()

◆ AdjacencyMatrixIndices()

◆ AdjacencyMatrixPrefix()

◆ AdjacencyMatrixWeights()

◆ ConnectedComponents() [1/2]

◆ ConnectedComponents() [2/2]

◆ ForEachEdge()

◆ GreedyColor()

◆ ListOfListsToAdjacency()

◆ MapToAdjacency()

◆ MatrixToAdjacency()

◆ MatrixToWeightedAdjacency()

◆ MeshAdjacencyMatrix() [1/2]

◆ MeshAdjacencyMatrix() [2/2]

◆ MeshDualGraph()

◆ MeshPrimalGraph()

◆ Partition()

◆ RemoveEdges()