qoolqit.graphs.data_graph

source module qoolqit.graphs.data_graph

Classes

DataGraph — The main graph structure to represent problem data.

source class DataGraph(edges: Iterable = [])

Bases : BaseGraph

The main graph structure to represent problem data.

Default constructor for the BaseGraph.

Parameters

edges : Iterable — set of edge tuples (i, j)

Attributes

adj — Graph adjacency object holding the neighbors of each node.
name — String identifier of the graph.
nodes — A NodeView of the Graph as G.nodes or G.nodes().
edges — An EdgeView of the Graph as G.edges or G.edges().
degree — A DegreeView for the Graph as G.degree or G.degree().
sorted_edges : set — Returns the set of edges (u, v) such that (u < v).
all_node_pairs : set — Return a list of all possible node pairs in the graph.
has_coords : bool — Check if the graph has coordinates.
has_edges : bool — Check if the graph has edges.
coords : dict — Return the dictionary of node coordinates.
node_weights : dict — Return the dictionary of node weights.
edge_weights : dict — Return the dictionary of edge weights.
has_node_weights : bool — Check if the graph has node weights.
has_edge_weights : bool — Check if the graph has edge weights.

Methods

line — Constructs a line graph, with the respective coordinates.
circle — Constructs a circle graph, with the respective coordinates.
random_er — Constructs an Erdős–Rényi random graph.
triangular — Constructs a triangular lattice graph, with respective coordinates.
hexagonal — Constructs a hexagonal lattice graph, with respective coordinates.
heavy_hexagonal — Constructs a heavy-hexagonal lattice graph, with respective coordinates.
random_ud — Constructs a random unit-disk graph.
from_matrix — Constructs a graph from a symmetric square matrix.
from_pyg — Create a graph from a pyg data object.
set_ud_edges — Reset the set of edges to be equal to the set of unit-disk edges.

source classmethod DataGraph.line(n: int, spacing: float = 1.0) → DataGraph

Constructs a line graph, with the respective coordinates.

Parameters

n : int — number of nodes.
spacing : float — distance between each node.

source classmethod DataGraph.circle(n: int, spacing: float = 1.0, center: tuple = (0.0, 0.0)) → DataGraph

Constructs a circle graph, with the respective coordinates.

Parameters

n : int — number of nodes.
spacing : float — distance between each node.
center : tuple — point (x, y) to set as the center of the graph.

source classmethod DataGraph.random_er(n: int, p: float, seed: int | None = None) → DataGraph

Constructs an Erdős–Rényi random graph.

Parameters

n : int — number of nodes.
p : float — probability that any two nodes connect.
seed : int | None — random seed.

source classmethod DataGraph.triangular(m: int, n: int, spacing: float = 1.0) → DataGraph

Constructs a triangular lattice graph, with respective coordinates.

Parameters

m : int — Number of rows of triangles.
n : int — Number of columns of triangles.
spacing : float — The distance between adjacent nodes on the final lattice.

source classmethod DataGraph.hexagonal(m: int, n: int, spacing: float = 1.0) → DataGraph

Constructs a hexagonal lattice graph, with respective coordinates.

Parameters

m : int — Number of rows of hexagons.
n : int — Number of columns of hexagons.
spacing : float — The distance between adjacent nodes on the final lattice.

source classmethod DataGraph.heavy_hexagonal(m: int, n: int, spacing: float = 1.0) → DataGraph

Constructs a heavy-hexagonal lattice graph, with respective coordinates.

Parameters

m : int — Number of rows of hexagons.
n : int — Number of columns of hexagons.
spacing : float — The distance between adjacent nodes on the final lattice.

Notes

The heavy-hexagonal lattice is a regular hexagonal lattice where each edge is decorated with an additional lattice site.

source classmethod DataGraph.random_ud(n: int, radius: float = 1.0, L: float | None = None) → DataGraph

Constructs a random unit-disk graph.

The nodes are sampled uniformly from a square of size (L x L). If L is not given, it is estimated based on a rough heuristic that of packing N nodes on a square of side L such that the expected minimum distance is R, leading to L ~ (R / 2) * sqrt(π * n).

Parameters

n : int — number of nodes.
radius : float — radius to use for defining the unit-disk edges.
L : float | None — size of the square on which to sample the node coordinates.

source classmethod DataGraph.from_matrix(data: ArrayLike) → DataGraph

Constructs a graph from a symmetric square matrix.

The diagonal values are set as the node weights. For each entry (i, j) where M[i, j] != 0 an edge (i, j) is added to the graph and the value M[i, j] is set as its weight.

Parameters