Structural Equivalence in Graphs and Complete Skeletons
Abstract.
Two vertices and of a graph are strucuturally equivalent if and only if the transposition is in Aut(), the automorphism group of . Some properties of structural equivalence and the group of vertex permutations generated by the transpositions in Aut() are discussed, along with the prime graphs of these groups. The notion of structural equivalence is used to develop a way of reconfiguring graphs into what are called their complete skeletons, which is closely related to compression graphs. Finally, the complete skeleton of a graph , denoted , is used to find a formula for rank, which is helpful for determining the multiplicity of the -1 eigenvalue of .
Key words and phrases:
graphs, automorphisms, eigenvalues2010 Mathematics Subject Classification:
Primary: 05C25, Secondary: 05C501. Introduction
The notion of vertex similarity in graphs is well understood. Two vertices and in a graph are said to be similar if there is some automorphism such that . This idea can be found in just about any graph theory textbook (see, for example, [3]). In this paper, we will consider a stronger notion of similarity of graph vertices known as structural equivalence. In particular, we can say that two vertices and in are structurally equivalent if their transposition is in Aut. In section 2, this idea will be considered in depth and it is used to construct a group for studying the structure of graphs, which we will call the structural equivalence permutation (SEP) group. This information is known in the literature, so its purpose is to familiarize the reader with this topic rather than establishing novel results.
In the third section, we study the prime graphs (or Gruenberg-Kegel graphs) of SEP groups of graphs. The prime graph of a group is obtained by taking the set of prime divisors of the group order as its vertex set and, if , if and only if there is an element in of order . Prime graphs of groups have been studied in many other contexts and a number of characterizations have already been proved. For example, it was proved in [8] that a graph is isomorphic to the prime graph of a solvable group if and only if is triangle-free and 3-colorable. In this paper, we are not concerned with proving new characterizations, but we are interested in seeing how they can inform us about the structure of SEP groups. Additionally, we will consider chains of prime graphs of SEP groups, proving that they must all terminate.
In the final two sections of the paper, we look at compressions of graphs and develop the idea of using complete skeletons for effectively conveying information about graphs after being compressed. Finally, we show that the problem of determining the multiplicity of the -1 eigenvalue for a graph can be reduced to a problem regarding the complete skeleton of , denoted .
2. Structurally Equivalent Vertices and the SEP Group
Two vertices of a graph are said to be similar if there is an automorphism , which maps to . The notion of vertex-transitive graphs arises when all vertices of a graph are similar. Here, though, we will consider a stronger, but related, condition on vertices in a graph.
Definition. Two vertices are structurally equivalent if and only if Aut. In other words, the permutation switching just the two vertices must be an automorphism.
From this definition, we see that two structurally equivalent vertices are essentially the same in an unlabelled graph, and if we swap two structurally equivalent vertices in a labelled graph, this does not affect the edge set. In the theorem below, we will use the notation to denote the th neighborhood of a vertex in a graph , meaning .
Theorem 2.1.
In a simple connected graph with at least two vertices, two connected vertices are structurally equivalent if and only if for all .
Proof.
Suppose . Clearly, because the transposition must preserve adjacency relations. Next, assume there is some such that . Thus, we can say without loss of generality that there is some such that . This cannot be, though, because , which is non-empty if . This means that a shortest path between and can also be a shortest path between and , simply by replacing the with the . Hence, for all , so . If , then the statement holds trivially.
Suppose for all . This means and can be swapped without affecting the adjacency relations of . Therefore, , which tells us that and are structurally equivalent.
∎
Note that it is important to subtract from and from because our graph is simple. However, this is only relevant when or 2 because, for any two structurally equivalent vertices and , . Hence, we need when and are adjacent and when and are not adjacent. We will keep the notation , though, so that we can cover both cases at once.
It is not difficult to see that structural equivalence forms an equivalence relation on the vertex set of a graph, which we see in the following theorem. In other words, the vertices of a graph can be partitioned into sets of structurally equivalent vertices. The proof is omitted because this is a well-known result in graph and network theory. Algorithms have been developed to determine these equivalence classes, which can be seen in [10]
Theorem 2.2.
Structural equivalence forms an equivalance relation on the vertex set of a graph.
Definition The structural equivalence classes of a graph are the sets of structurally equivalent vertices that partition .
For the remainder of the paper, we will simply use "equivalence classes" to refer to the structural equivalence classes in a graph.
Just as we can discuss the automorphism group of a graph, so can we formulate a group of permutations between structurally equivalent vertices in a graph, which we define below.
Definition. For a graph , we call the group of permutations between structurally equivalent vertices in the structural equivalence permutation group of , which we write as .
One way that we can express this group is
Because structural equivalence is an equivalence relation, it is easy to see that the SEP group of an equivalence class is just the symmetric group when there are vertices in the equivalence class. Hence, because has just one equivalence class, we see . However, for any graph that is not complete, there are at least two equivalence classes, so has a minimum of two symmetric permutation subgroups on disjoint sets of vertices. We will let denote the number of equivalence classes for . Next, we let the permutation group on the th equivalence class be written as , where is the number of vertices in this th equivalence class. Also, note that for because the symmetric groups are on disjoint sets of vertices. This group is valuable for the studying of chemical structures, as seen in [2]. Also, algorithms have been designed to determine these groups for particular graphs (see [1] and [5]).
Theorem 2.3.
We can write the SEP group of as
(1) |
Also, .
Proof.
Clearly, because there is some such that for all , which tells us that all the generators of are included in . Next, consider some . Because each of the groups consist of the permutations on distinct vertex sets, the smallest subgroup containing for all is the group consisting of all possible compositions of distinct elements from each of the . Because for , we see that can be expressed as the direct product of the , i.e. . We find because each of the can be generated by the transpositions of its vertices, and each element in , as we saw, is a composition of distinct elements from the , so can be generated by the transpositions in Aut via compositions. The formula for the order of follows immediately from the fact that . ∎
Just as the automorphism group of a graph conveys useful information about the structure of a graph, so does the SEP group. It is also not difficult to see that the SEP group is a subgroup of the automorphism group and it conveys a stronger notion of graph symmetry. Any two elements in a same permutation cycle in can be transposed without affecting the adjacency relations of the graph. This is not necessarily the case for the automorphism group. This can be seen through the example in Figure 2. If we denote this graph as , then we find that only consists of the identity permutation and . Consider the permutation . This is a valid automorphism, so it is in Aut, but neither nor are in the automorphism group, which would be the case if . We see that this generalizes to the notion that has the following hereditary property:
Theorem 2.4.
(Hereditary Property of ) If , then any proper sub-cycle of a cycle in is in . Also, if consists of the compositions of disjoint cycles, then each of the disjoint cycles are in .
In other words, if we have some cycle , then we also know and are in . For the case where is a transposition, any proper subcycle is the identity permutation, and if consists of a collection of disjoint cycles, we can apply this to each of the cycles separately.
Proof.
The theorem is clearly true for the case where the proper sub-cycles are transpositions, given that is generated by a set of transpositions. Any other proper sub-cycle must also be an element of a unique , so by Theorem 2.3. Similar reasoning can be applied to the disjoint cycles composing . ∎
In the following section, we will consider some properties of prime graphs of the SEP groups of graphs.
3. Prime Graphs of SEP Groups of Graphs
Recall the definition of the prime graph of a group. For a group , we define the prime graph of as the graph , where is the set of prime numbers dividing the order of and, for any two of these primes and , if and only if there is an element in of order . Next, we will need to define some new notation. We let be and be the prime graph of . When we are only concerned with the first prime graph of for some graph , we will write instead of . We will also adjust some of our notation for the permutation groups on the equivalence classes of a graph. The such that is the greatest shall be denoted as , and we will use to denote the on the second largest equivalence class. Thus, for graphs with at least two equivalence classes, we can write
Theorem 3.1.
If a graph has only one (structural) equivalence class on vertices, then is the set of primes that are less than or equal to , and for any two of these primes , if and only if . If has at least two equivalence classes, such that and are the sizes of the largest and second smallest equivalence classes, then is still the set of primes that are less than or equal to , but for two such primes, and , if and only if or, if , and .
Proof.
Suppose first that has only one equivalence class. Then , where is the size of this equivalence class, as expressed in the theorem. First, we observe
because , so the primes that divide are precisely the primes less than or equal to . If and are primes, then the only way to get an element of order in is if we can compose two disjoint cycles of sizes and , which we can do if and only if .
Next, suppose has at least two equivalence classes. Then, we can say
It is clear that reasoning from above also applies to this case, so all we have left to show is that for , even if , can still be an edge in , given that and (where ). This follows from the fact that and are permutation groups on different equivalence classes. If there is a cycle of size in and a cycle of size in , then the theorem follows. ∎
In the next theorem, we will characterize graphs by the presence -cliques, which will also enable us to determine for which graphs the prime graph of is complete. First, we will let denote the th prime number.
Theorem 3.2.
If , then has a -clique if and only if or and . If , then has a -clique if and only if .
By Theorem 3.1, we can deduce that if has a -clique, then this clique is on the first primes in the labelled prime graph. Now, we will prove the theorem.
Proof.
First, consider the case where has at least two equivalence classes, or . By Theorem 3.1, we can deduce that implies that for all . Hence, we just need to find the conditions under which is true in order to prove that has a -clique. However, this is also taken care of by Theorem 3.1, so the first case is proved. The second case follows by similar reasoning. ∎
Corollary 3.3.
for if and only if such that and .
Proof.
Suppose is a complete graph on at least two vertices. Thus, it contains a -clique and ; otherwise, there would be at least vertices. By Theorem 3.2, such that or and , or is just , where . However, it is known that there is no such that (This can be seen, for example, in [6]). Thus, we see that the only option for is such that and . The reverse direction follows immediately from Theorem 3.2 and restriction . ∎
Theorem 3.4.
for all such that .
Proof.
First, we will show . Suppose . We note is isomorphic to the direct product of symmetric groups on the vertices in the equivalence classes of . Because by Theorem 2.3, we note
(Note that the first set inclusion is an equality in the case that is complete.) Thus, we have found . Note that in the special case where , , so is also empty. If , then it makes no sense to consider (although, we could say that, in a trivial sense, , the empty graph).
All we have left to prove is that the edge set of is contained in the edge set of . Consider the two graphs as labelled prime graphs and assume that there is some edge such that . Because we have seen , it follows from Theorem 3.1 that the largest equivalence class in has fewer vertices than the largest equivalence class in . Let and be the permutation groups on these largest equivalence classes for and , respectively. Define and similarly for the permutation groups on the second largest equivalence classes, if they exist. Hence, we have found . Also, by Theorem 3.1, we know that }. Because equivalence classes partition the vertex set of a graph, we can say . Next, we can use Theorem 3.1 again to determine that either or and (where ) because . First, suppose . Then, we find because . This is a contradiction, though, because this implies . Next, suppose and . We find , so we have another contradiction. Thus, if , then , which completes the proof.
∎
With this observation, the next result follows immediately.
Corollary 3.5.
There is no graph such that .
Embedded in this statement is the fact that there is no such that for some "initial graph" . This is because we can just let be .
Definition. Let be the SEP -series. If is the final element in the series before the empty graph, we say that is the minimal element in the SEP -series and is the length of the SEP -series.
Theorem 3.6.
Every SP -series has a minimal element (i.e. has a finite length).
Proof.
By Theorem 3.4 and well-ordering, we know that there is an such that is minimal in the poset of the graphs with respect to . The theorem follows. ∎
4. The Connection Between Structural Equivalence and rank
Now, we will look at how structurally equivalent vertices affect the adjacency matrix of a graph. We will use the standard notation, , to denote the adjacency matrix of a graph . The following theorem, regarding edges within equivalence classes, can be found in [9].
Theorem 4.1.
If a class of structurally equivalent vertices has an edge, then the induced subgraph on the equivalence class of vertices is complete.
Any graph can be expressed as a new graph where the vertices represent complete induced subgraphs of and if there is an edge between any two of these complete induced subgraphs and , this indicates that all the vertices in are completely connected with the all the vertices in . In other words, the edges denote the existence of induced complete bipartite graphs between two complete graphs (which is ultimately a larger complete graph). Note that for many graphs, if we try to reconfigure them in this way, we will find that every vertex is just and every edge only represents one edge in , which means that this reconfiguration of is isomorphic to . Such a reconfiguration of would, thus, be considered trivial. However, there is a considerable number of graphs such that this form of reconfiguration is non-trivial. Next, we will provide a definition and an example to clarify.
Definition. The complete skeleton of a graph , denoted , is the smallest reconfiguration of such that the vertices of represent complete induced subgraphs of and the edges represent the complete connection of edges between the two complete induced subgraphs that it connects.
The complete skeleton of a graph , as defined above, is very closely related with the compression graph of . Compression graphs have been considered previously (see [9]), but we will approach this problem in a slightly different way.
Figure 3 provides an example to help visualize what complete skeletons of graphs may look like. The reader is encouraged to convince him- or herself that the example is, in fact, true before moving on. In some of the following theorems, we will draw the connection between complete skeletons and equivalence classes. Before doing so, though, it will be helpful to define one more term.
Definition. For any reconfiguration of a graph via the reinterpretation of vertices and edges given above, which we will write as , we say that two vertices and in can be conflated if and only if they are connected and . The conflation of and results in a single vertex , where all the vertices incident to are the edges that were incident to or , excluding the edge between the two.
By this new definition, we see that if and only if no vertices in can be conflated.
Theorem 4.2.
The vertices of are the equivalence classes of , with one possible exception. If a collection of independent vertices are and share the same neighbors, they form a completely disconnected equivalence class.
Proof.
First, we will consider the case where there are no vertices in that form a disconnected equivalence class. It is apparent that any of the vertices of represented by one of the vertices in are structurally equivalent, so we just need to show that the vertices of are the complete equivalence classes. This follows from the fact that is the smallest possible reconfiguration of under the reinterpretation of vertices and edges. If there were structurally equivalent vertices in two distinct vertices and of , we could conflate the two vertices into a single vertex , where the set of edges incident to would be the set of edges incident to or (which are the same because they are structurally equivalent). However, because is the smallest possible reconfiguration of under the reinterpretation of vertices and edges, no vertices can be conflated in this way, so we deduce that the vertices are the complete equivalence classes.
Now, we will consider the case with disconnected structurally equivalent vertices separately. Clearly, if we have two or more vertices in that share the same neighbors, these vertices are not their own complete equivalence classes because we can form a larger one containing them. The paragraph above applies to the rest of the graph, though– it is easy to observe that any of the remaining vertices of are still their own complete equivalence classes.
∎
Corollary 4.3.
No two connected vertices in are structurally equivalent.
Proof.
This follows from reasoning similar to what was used to prove Theorem 4.2. If two vertices are connected and are structurally equivalent in , then we can conflate them as we saw above, which contradicts the minimality of . ∎
Definition. We will let the structure of a complete skeleton of a graph , which we will write as or just when we are not concerned about the particular , be the reinterpretation of where the vertices and edges are interpreted again in the standard way.
To clarify, the structure of in Figure 3 is . Note that is what is typically called the compression graph of . The vertices and edges of are often called super-nodes and super-edges since they denote collections of nodes or edges in . The primary value of complete skeletons, though, is that they uniquely represent graphs while maintaining the structures of their compressions.
Theorem 4.4.
For a graph , if and only if has no connected structurally equivalent vertices.
Proof.
The forward direction was established by Corollary 4.3. Now, we just need to show that any graph without connected structurally equivalent vertices can be the structure of a complete skeleton. Let be such a graph. Reinterpret the vertices and edges of such that the vertices represent complete induced subgraphs of some other graph and two of these vertices are connected if and only if the two complete induced subgraphs are completely connected. Because contains no connected structurally equivalent vertices, there are no connected vertices and in such that . Thus, no vertices in can be conflated, so , which means . ∎
These new results allow us to come up with a formula for rank; next, we will find an upper bound for this.
Theorem 4.5.
We can calculate rank by the equation:
(2) |
Proof.
Consider the matrix . This is precisely the adjacency matrix of where a loop is attached to each vertex. Let this new graph be . If two vertices and were connected and structurally equivalent in , then we see in , so connected structurally equivalent vertices have identical columns in rows in , so they must be linearly dependent. If and were disconnected structurally equivalent vertices in , then in . This means that for disconnected structurally equivalent vertices, their columns and rows differ in two places (corresponding to the coordinates of the two vertices) in . Hence, the rows and columns for any disconnected structurally equivalent vertices must be linearly independent with respect to each other, so we cannot a priori establish a lower upper bound for rank(. Thus, we can conclude rank is bounded above by the sum of the number of connected equivalence classes in with the number of the individual disconnected structurally equivalent vertices. Equivalently, rank. ∎
Using this result, we can also find a new expression for determining whether a graph has -1 as an eigenvalue and what its multiplicity is.
Lemma 4.6.
If the multiplicity of the -1 eigenvalue for a simple graph is , then .
Proof.
It is well known in spectral graph theory that, for a graph on vertices, the multiplicity of an eigenvalue is given by , so the theorem follows from this and Theorem 4.5. ∎
Because the inequality in Theorem 4.5 provides us with a lower bound, we can correct this with a small constant term. If we denote this term by , then we find
(3) |
so
(4) |
Because we now have an equality, we can provide the following characterization of graphs by the multiplicity of the -1 eigenvalue.
Theorem 4.7.
A graph has -1 as an eigenvalue of multiplicity if and only if
(5) |
Unfortunately, this does not give us a purely graph theoretic method for determining the multiplicity of the -1 eigenvalue. For this to be the case, we would need a way to interpret without determining for each complete skeleton structure. At least for small , we find that is almost always 0. As stated in the next section, the only complete skeleton structure with five or fewer vertices such that is , for which . For all the other cases where , though, we can rewrite Equation 5 as
which effectively reduces the linear algebraic problem to a graph theoretic problem. For the general case, however, further research must be completed before we can solve this problem of determining the multiplicity of the -1 eigenvalue in purely graph theoretic terms.
5. Complete Skeletons and the multiplicity of the -1 eigenvalue
Previous literature has already considered the possible graph structures such that . We will present these known results, translate them into the language of complete skeletons, and then we will then use some of the results from the previous section to consider some possible graphs such that rank or 5. Or, in other words, for graphs on vertices, we will find graphs that have -1 as an eigenvalue with multiplicity or .
Theorem 5.1.
If rank, then is complete.
Theorem 5.2.
If rank, then is the union of two disjoint complete graphs.
Theorem 5.3.
If rank such that has vertices, then such that and or such that and .
These three theorems can be found in [4]. The following three results are the equivalent expressions of the theorems above, using the terminology of complete skeletons. They will be presented without proof, for it should be easy to see how they follow from Theorems 5.1, 5.2, and 5.3.
Theorem 5.1’. If rank, then is a single vertex.
Theorem 5.2’. If rank, then is two disconnected vertices.
Theorem 5.3’. If rank, then is three disconnected vertices or a 3-path.
Next, we will consider graphs such that rank. Because our characterization in Theorem 4.7 is not purely graph theoretic, we are unable to make the same kind of statement for these higher rank cases. In particular, our conditionals must consist of graph theoretic properties in the antecedent with the linear algebraic term as the consequent. Also, for the remainder of the paper, our inquiry will be guided by the number of vertices in complete skeletons.
Theorem 5.4.
If is one of the graphs in Figure 4, then rank.
Proof.
The graphs in Figure 4 are all the possible complete skeletons of four vertices. This can be verified by checking all graphs of four vertices and finding the ones that have no connected structurally equivalent vertices. By Theorem 4.4, these graphs can be complete skeleton structures of graphs. Additionally, by Theorem 4.5, we know that for any graph that has one of these five graphs as its complete skeleton, rank. It can easily be shown that for all of these graphs, so rank. ∎
Of course, it follows from this result that, if a graph of vertices has one of the graphs in Figure 4 as its complete skeleton, then -1 is an eigenvalue of with multiplicity . Figures 5 and 6 provide several examples of graphs that fall into this category.
We can follow a very similar procedure for determining graphs such that rank through finding their possible complete skeletons. This is what we will accomplish in the next theorem.
Theorem 5.5.
Proof.
There are 34 total graphs with five vertices, which can all be found in [7]. As we saw in the proof of Theorem 5.4, we can find the possible complete skeletons of five vertices by determining all graphs with five vertices that contain no two connected structurally equivalent vertices; these graphs are the structures of the complete skeletons. The reader is encouraged to verify that the fifteen graphs in Figure 7 are all such graphs. Further, it can easily be verified with a CAS that for all of the graphs in Figure 7, excluding the second one in the second row. In other words, rank unless . In this case, there is linear dependence among the columns of and, in particular, rank, so and rank by Equation 3. ∎
Clearly, it grows increasingly tedious to find all of the possible complete skeletons of graphs with a given number of vertices as this number grows larger. Additionally, it is not particularly interesting, so we will not proceed to consider the six vertex case, even though this could be done. If one were to pursue this question, the theory has now been sufficiently developed to do so with relative ease. As we have seen, all one needs to do in order to find all of the possible complete skeleton structures with vertices is to find all of the graphs of vertices such that no two connected vertices are structurally equivalent and let these be the structures of the possible complete skeletons. Also, as we have seen, it is one thing to determine all the possible complete skeletons for a given number of vertices, but it would require further work to find for all these graphs. By what we saw, it seems reasonable to conjecture that is "often" 0, but it is difficult at this point to tell how strong "often" is. It certainly is too early to conjecture that is almost always 0. However, if the problem of determining in general could be solved, this would provide a characterization of graphs by the multiplicity of the -1 eigenvalue that is clean and easy to work with.
6. Acknowledgements
This work originated while the author participated in an REU-program (NSF-REU grant DMS-1757233) run virtually at Texas State University during Summer 2020, directed by Yong Yang (PI) and Thomas M. Keller (co-PI).
References
- [1] K. Balasubramanian “Computational Techniques for the Automorphism Groups of Graphs” In J Chem Inform Comput Sci 34, 1994, pp. 621–626
- [2] S. Bohanec and M. Perdih “Symmetry of chemical structures: a novel method of graph automorphism group determination” In J Chem Inform Comput Sci 33, 1993, pp. 719–726
- [3] J.A. Bondy and U.S.R Murty “Graph Theory”, Graduate Texts in Mathematics 175 Fifth Avenue, New York, NY 10010, USA: Springer-Verlag New York, Inc., 2008
- [4] Marc Cámara and Willem H. Haemers “Spectral Characterizations of Almost Complete Graphs” In Discret. Appl. Math 176, 2014, pp. 19–23
- [5] Jean-Loup Faulon “Isomorphism, Automorphism Partitioning, and Canonical Labeling Can Be Solved in Polynomial-Time for Molecular Graphs” In J Chem Inform Comput Sci 38, 1998, pp. 432–444
- [6] “For consecutive primes , prove that ”, MathOverflow, 2012 URL: https://mathoverflow.net/questions/113840/for-consecutive-primes-a-lt-b-lt-c-prove-that-ab-ge-c
- [7] “Graphs ordered by number of vertices”, Information System on Graph Classes and their Inclusions URL: https://www.graphclasses.org/smallgraphs.html#order_by_number
- [8] Alexander Gruber et al. “A characterization of the prime graphs of solvable groups” In J. Algebra 442, 2015, pp. 397–422
- [9] Thien Nguyen et al. “Applications of Structural Equivalence to Subgraph Isomorphism on Multichannel Multigraphs” In 2019 IEEE International Conference on Big Data, 2019
- [10] Malcolm K. Sparrow “A linear algorithm for computing automorphic equivalence classes: the numerical signatures approach” In Soc Networks 15, 1993, pp. 151–170