Weisfeiler Leman Graph Isomorphism Test

In graph theory, the Weisfeiler Leman graph isomorphism test is a heuristic test for the existence of an isomorphism between two graphs ''G'' and ''H''. It is based on a normal form for graphs first described in an article by Weisfeiler and Leman in 1968. There are several versions of the test referred to in the literature by various names, which easily leads to confusion.

The basic Weisfeiler Leman graph isomorphism test

The basic version called WLtest takes a graph as input, produces a partition of the nodes which is invariant under automorphisms and outputs a string certificate encoding the partition. When applied to two graphs ''G'' and ''H'' we can compare the certificates. If the certificates do not agree the test fails and ''G'' and ''H'' cannot be isomorphic. If the certificates agree, ''G'' and ''H'' may or may not be isomorphic.

The partition is produced in several rounds starting from the trivial partition where all nodes belong to the same component. In each round the partition is refined, which means that once two nodes are in different components they will also be in different components at future rounds. The partition refinement stops when the partition from the last round and the current partition are the same. This means that the refinement stops after $n-1$ rounds where ''n'' is the number of nodes in the graph. This is because the number of components need to grow by at least 1 in each step and the number of components cannot exceed ''n''.

Membership of a node in a component is indicated by a label. In each round labels are recomputed and refined. After termination of the refinement, stringing together the labels after sorting yields the certificate. In order for isomorphic graphs to produce the same certificate one has to be very cautious with the labels because an isomorphism may swap nodes and may modify the order in which nodes are processed which in turn may alter the certificate. As an example the labels in the end may be 2-fold ''x'', a single ''y'' and 2-fold ''z'' for one graph and a 2-fold ''x'', 2-fold ''y'' and single ''z'' for an isomorphic graph yielding after sorting and stringing ''x_x_y_z_z'' resp. ''x_x_y_y_z''.

One can be less careful with the labels, if one applies WLtest to a single graph created as the disjoint union of the two graphs ''G'' and ''H''. Applying to the disjoint union will ensure that both graphs are processed in parallel at each stage, which will avoid such a swap of labels, say ''x'' and ''y'' as in the example above. Additionally the parallel processing can distinguish even more pairs of non-isomorphic graphs, see the examples below. In what follows we will discuss this version WLpairs.

The refinement of the partition in each step is by processing for each node its label and the labels of its nearest neighbors. Therefore WLtest can be viewed as a message passing algorithm.

Code

# ALGORITHM WLpairs
# INPUT: two graphs G and H to be tested for isomorphism
# OUTPUT: Certificates for G and H and whether they agree
U = combineTwo(G, H)
glabels = initializeLabels(U) # dictionary where every node gets the same label 0
labels = # dictionary that will provide translation from a string of labels of a node and its neighbors to an integer
newLabel = 1
done = False
while not(done):
glabelsNew = # set up the dictionary of labels for the next step
for node in U:
label = str(glabelsode + str( labels[xfor x in neighbors of node">.html" ;"title="labels[x">labels[xfor x in neighbors of nodesort())
if not(label in labels): # a combination of labels from the node and its neighbors is encountered for the first time
labelsabel= newLabel # assign the string of labels to a new number as an abbreviated label
newLabel += 1 # increase the counter for assigning new abbreviated labels
glabelsNewode= labelsabel if (number of different labels in glabels)

(number of different labels in glabelsNew):
done = True
else:
glabels = glabelsNew.copy()
certificateG = certificate for G from the sorted labels of the G-part of U
certificateH = certificate for H from the sorted labels of the H-part of U
if certificateG

certificateH:
test = True
else:
test = False

Here is some actual Python code which includes the description of the first examples.

g5_00 =
g5_01 =
g5_02 =

def combineTwo(g1, g2):
g =
n = len(g1)
for node in g1:
s = set()
for neighbor in g1ode
s.add(neighbor)
gode= s.copy()
for node in g2:
s = set()
for neighbor in g2ode
s.add(neighbor + n)
g ode + n= s.copy()
return g

g = combineTwo(g5_00, g5_02)
labels =
glabels =
for i in range(len(g)):
glabels = 0
glabelsCount = 1
newlabel = 1

done = False
while not (done):
glabelsNew =
glabelsCountNew = 0
for node in g:
label = str(glabelsode
s2 = []
for neighbor in gode
s2.append(glabels eighbor
s2.sort()
for i in range(len(s2)):
label += "_" + str(s2
if not (label in labels):
labelsabel= newlabel
newlabel += 1
glabelsCountNew += 1
glabelsNewode= labelsabel if glabelsCount

glabelsCountNew:
done = True
else:
glabelsCount = glabelsCountNew
glabels = glabelsNew.copy()
print(glabels)

g0labels = []
for i in range(len(g0)):
g0labels.append(glabels
g0labels.sort()
certificate0 = ""
for i in range(len(g0)):
certificate0 += str(g0labels + "_"
g1labels = []
for i in range(len(g1)):
g1labels.append(glabels[i + len(g0)])
g1labels.sort()
certificate1 = ""
for i in range(len(g1)):
certificate1 += str(g1labels + "_"

if certificate0

certificate1:
test = True
else:
test = False
print("Certificate 0:", certificate0)
print("Certificate 1:", certificate1)
print("Test yields:", test)

Examples

The first three examples are for graphs of order 5.

WLpair takes 3 rounds on 'G0' and 'G1'. The test succeeds as the certificates agree.

WLpair takes 4 rounds on 'G0' and 'G2'. The test fails as the certificates disagree. Indeed 'G0' has a cycle of length 5, while 'G2' doesn't, thus 'G0' and 'G2' cannot be isomorphic.

WLpair takes 4 rounds on 'G1' and 'G2'. The test fails as the certificates disagree. From the previous two instances we already know $G_1\cong G_0\not\cong G_2$.

Indeed ''G0'' and ''G1'' are isomorphic. Any isomorphism must respect the components and therefore the labels. This can be used for kernelization of the graph isomorphism problem. Note that not every map of vertices that respects the labels gives an isomorphism. Let $\varphi: G_0\rightarrow G_1$ and $\psi: G_0\rightarrow G_1$ be maps given by $\varphi(a)=D,\varphi(b)=C,\varphi(c)=B,\varphi(d)=E,\varphi(e)=A$ resp. $\psi(a)=B,\psi(b)=C,\psi(c)=D,\psi(d)=E,\psi(e)=A$. While $\varphi$ is not an isomorphism $\psi$ constitutes an isomorphism.

When applying WLpair to ''G0'' and ''G2'' we get for ''G0'' the certificate ''7_7_8_9_9''. But the isomorphic ''G1'' gets the certificate ''7_7_8_8_9'' when applying WLpair to ''G1'' and ''G2''. This illustrates the phenomenon about labels depending on the execution order of the WLtest on the nodes. Either one finds another relabeling method that keeps uniqueness of labels, which becomes rather technical, or one skips the relabeling altogether and keeps the label strings, which blows up the length of the certificate significantly, or one applies WLtest to the union of the two tested graphs, as we did in the variant WLpair. Note that although ''G1'' and ''G2'' can get distinct certificates when WLtest is executed on them separately, they do get the same certificate by WLpair.

The next example is about regular graphs. WLtest cannot distinguish regular graphs of equal order, but WLpair can distinguish regular graphs of distinct degree even if they have the same order. In fact WLtest terminates after a single round as seen in these examples of order 8, which are all 3-regular except the last one which is 5-regular.

All four graphs are pairwise non-isomorphic. ''G8_00'' has two connected components, while the others do not. ''G8_03'' is 5-regular, while the others are 3-regular. ''G8_01'' has no 3-cycle while ''G8_02'' does have 3-cycles.

Another example example of two non-isomorphic graphs that WLpair cannot distinguish is given here.

Applications

The theory behind the Weisfeiler Leman test is applied in graph neural networks.

Weisfeiler Leman graph kernels

In machine learning of nonlinear data one uses kernels to represent the data in a high dimensional feature space after which linear techniques such as support vector machines can be applied. Data represented as graphs often behave nonlinear. Graph kernels are method to preprocess such graph based nonlinear data to simplify subsequent learning methods. Such graph kernels can be constructed by partially executing a Weisfeiler Leman test and processing the partition that has been constructed up to that point. These Weisfeiler Leman graph kernels have attracted considerable research in the decade after their publication. They are also implemented in dedicated libraries for graph kernels such as GraKeL.

Note that kernels for artificial neural network in the context of machine learning such as graph kernels are not to be confused with kernels applied in heuristic algorithms to reduce the computational cost for solving problems of high complexity such as instances of NP-hard problems in the field of complexity theory. As stated above the Weisfeiler Leman test can also be applied in the later context.

See also

* Graph isomorphism
* Graph neural network

References

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Articles with example Python (programming language) code
Graph theory