FlexRiLoG - Rigid and Flexible Labelings of Graphs

Basic notions

Definition

Let $G=(V_G,E_G)$ be a graph with an edge labeling $\lambda:E_G\rightarrow \mathbb{R}_+$.

A realization $\rho:V_G\rightarrow\mathbb{R}^2$ is called compatible with $\lambda$ if $||\rho(u)-\rho(v)||=\lambda(uv)$ for all $uv\in E_G$.

The labeling $\lambda$ is called

  • (proper) flexible if the number of (injective) realizations of $G$ compatible with $\lambda$ is infinite,
  • rigid if the number of realizations of $G$ compatible with $\lambda$ is finite and positive,

where the counting is up to direct Euclidean isometries. A graph is called movable iff it has a proper flexible labeling.

In [ ]:
from flexrilog import GraphGenerator
from flexrilog import FlexRiGraph
C4 = FlexRiGraph([[0,1],[1,2],[2,3],[0,3]], name='4-cycle', pos={0:(0,0),1:(1,0), 2:(0.8,0.8), 3:(0.3,0.6)})
S = GraphGenerator.SmallestFlexibleLamanGraph()
T = GraphGenerator.ThreePrismGraph()
M = GraphGenerator.MaxEmbeddingsLamanGraph(7)
K33 = FlexRiGraph(graphs.CompleteBipartiteGraph(3,3))
N = FlexRiGraph(448412, pos={0 : (-0.5,-0.75), 1 : (0.5,0.5), 2 : (1.5,0.5), 3 : (2.5,-0.75),
                                       4 : (0.5,1.5), 5 : (1.5,1.5), 6 : (1,-0.25)}, name='No NAC')
Q1 = GraphGenerator.Q1Graph()
examples = [C4, S, T, M, K33, N, Q1]
# show(*[G.plot().show(figsize=[4,4], dpi=80) for G in examples])
figs = graphics_array([G.plot() for G in examples],
                      ncols=3)
figs.show(figsize=[10,5], axes=False)

Theorem [Pollaczek-Geiringer, Laman]

A graph is generically rigid, i.e., a generic realization defines a rigid labeling, if and only if the graph contains a Laman subgraph with the same set of vertices.

A graph $G=(V_G,E_G)$ is called Laman if $|E_G| = 2|V_G|-3$, and $|E_H|\leq 2|V_H|-3$ for all subgraphs $H$ of $G$.

In [ ]:
table([['', 'is Laman']]+[[G.name(), str(G.is_Laman())] for G in examples])

Flexible labelings

Definition

Let $G$ be a graph. A coloring of edges $\delta\colon E_G\rightarrow \{\text{blue, red}\}$ is called a NAC-coloring, if it is surjective and for every cycle $C$ in $G$, either all edges of $C$ have the same color, or $C$ contains at least 2 edges in each color.

Theorem [Grasegger, L., Schicho]

A graph $G$ has a flexible labeling if and only if it has a NAC-coloring.

In [ ]:
table([['', 'has NAC-coloring']]+[[G.name(), str(G.has_NAC_coloring())] for G in examples])
In [ ]:
for G in [C4, S, T]:
    print(G.name())
    G.show_all_NAC_colorings()

Motion construction from a NAC-coloring

In [ ]:
from flexrilog.graph_motion import GraphMotion
delta = T.NAC_colorings()[0]
motion_T = GraphMotion.GridConstruction(T, delta)
motion_T.parametrization()
In [ ]:
motion_T.animation_SVG(edge_partition='NAC')
In [ ]:
motion_T = GraphMotion.GridConstruction(T, delta, zigzag=[[[0,0], [3/4,1/2], [2,0]], [[0,0], [1,0]]])
motion_T.parametrization()
In [ ]:
motion_T.animation_SVG()
In [ ]:
delta = S.NAC_colorings()[0]
delta.plot().show(figsize=[4,4])
motion_S = GraphMotion.GridConstruction(S, delta, check=False)
motion_S.animation_SVG(edge_partition='NAC')
In [ ]:
table([['', 'NAC-coloring', 'injective grid'
       ]]+[[G.name(), str(G.has_NAC_coloring()), str(G.has_injective_grid_construction())] for G in examples])

Movable graphs

Recall - we look for a proper flexible labeling, i.e., infinitely many injective realizations

Definition $\DeclareMathOperator{\CDC}{CDC} \newcommand{\cdc}[1]{\CDC(#1)}$ $\DeclareMathOperator{\Upairs}{U} \newcommand{\upairs}[1]{\Upairs(#1)}$

Let $\upairs{G}$ denote the set of all pairs $\{u,v\}\subset V_G$ such that $uv\notin E_G$ and there exists a path from $u$ to $v$ which is unicolor for all NAC-colorings $\delta$ of $G$.
If there exists a sequence of graphs $G=G_0, \dots, G_n$ such that $G_i=(V_{G_{i-1}},E_{G_{i-1}} \cup \upairs{G_{i-1}})$ for $i\in\{1,\dots,n\}$ and $\upairs{G_n}=\emptyset$, then the graph $G_n$ is called the constant distance closure of $G$, denoted by $\cdc{G}$.

Theorem [Grasegger, L., Schicho]

A graph $G$ is movable if and only $\cdc{G}$ is movable.

In [ ]:
CDC = M.constant_distance_closure()
M.show_all_NAC_colorings()
CDC.plot()

Corollary

If $G$ is movable, then $\cdc{G}$ is not complete.

In [ ]:
table([['', 'NAC-coloring', 'injective grid', 'CDC non-complete']]+
      [[G.name(), str(G.has_NAC_coloring()), 
        str(G.has_injective_grid_construction()), str(not G.cdc_is_complete())] for G in examples])

Lemma [Grasegger, L., Schicho]

Let $G=(V,E)$ be a graph with an injective embedding $\omega:V\rightarrow\mathbb{R}^3$ such that for every edge $uv\in E$, the vector $\omega(u)-\omega(v)$ is parallel to one of the four vectors $(1,0,0)$, $(0,1,0)$, $(0,0,1)$, $(-1,-1,-1)$, and all four directions are present. Then $G$ is movable.

Moreover, there exist two NAC-colorings such that two edges are parallel in the embedding $\omega$ if and only if they receive the same pair of colors.

In [ ]:
res, NACs = Q1.has_injective_spatial_embedding(certificate=True)
print(res)
graphics_array([col.plot() for col in NACs])
In [ ]:
motion_Q1 = GraphMotion.SpatialEmbeddingConstruction(Q1,NACs)
motion_Q1.fix_edge([5,6])
motion_Q1.parametrization()
In [ ]:
motion_Q1.animation_SVG()
In [ ]:
table([['', 'movable', 'reason']]+
      [[G.name()]+list(G.is_movable()) for G in examples])
In [ ]:
S1 = GraphGenerator.S1Graph()
show(S1)
S1.is_movable()

Motion Classification - $K_{3,3}$

In [ ]:
from flexrilog import MotionClassifier
In [ ]:
G = GraphGenerator.K33Graph()
MC = MotionClassifier(G)
In [ ]:
motion_classes = MC.possible_motion_types_and_active_NACs({0:'Dixon I',
                                          3: 'special case of Dixon I',
                                          7: 'special case of Dixon I',
                                          4: 'Dixon II',
                                         })
In [ ]:
dixonII_mt = motion_classes[2][0]; dixonII_mt

edge lengths enforced by motion types:

In [ ]:
MC.show_factored_eqs(MC.motion_types2same_lengths_equations(dixonII_mt))

active NAC-colorings:

In [ ]:
active_NACs = MC.motion_types2active_NACs(dixonII_mt)
In [ ]:
MC.singletons_table(active_NACs)

equations from motion types and comparing leading coefficients:

In [ ]:
MC.show_factored_eqs(MC.motion_types2equations(dixonII_mt))
In [ ]:
 
In [ ]:
 
In [ ]: