Eulerian Magnitude Homology
Posted by Tom Leinster
Guest post by Giuliamaria Menara
Magnitude homology has been discussed extensively on this blog and definitely needs no introduction.
A lot of questions about magnitude homology have been answered and a number of possible application have been explored up to this point, but magnitude homology was never exploited for the structure analysis of a graph.
Being able to use magnitude homology to look for graph substructures seems a reasonable consequence of the definition of boundary map . Indeed, a tuple is such that if for every vertex it holds that . In other words, if every vertex of the tuple is contained in a small enough substructure, which suggests the presence of a meaningful relationship between the rank of magnitude homology groups of a graph and the subgraph counting problem.
A major problem in exploring this relationship comes from the fact that the definition of only asks for consecutive vertices to be different. That is, if and are two adjacent vertices in an acceptable tuple in is .
Tuples of this kind inducing a path that just revisits again and again the same edge (an more in general, tuples inducing non-eulerian trails) do not provide any insight about the meaning of magnitude homology. With this motivation, we introduce a slightly different definition of magnitude chain, considering the subgroup of where a vertex (and therefore an edge) is never required to be revisited.
Definition (Eulerian magnitude chain) Let be a graph. We define the eulerian -magnitude chain to be the free abelian group generated by tuples of vertices of such that for all distinct and .
Taking as differential the one induced by we can construct the eulerian magnitude chain complex :
and subsequently define the eulerian -magnitude homology group
Example Consider the following graph :
We want to compare and .
The magnitude chain is generated by
while is generated by
We see that is generated by all elements in apart from
and thus has rank 14.
On the other hand, the eulerian chain is generated by
while is generated by
We have now that is generated by all elements in apart from
and thus has rank 6, as the number of permutations of the triangle [1,2,3].
Remark We point out that all definitions and properties regarding magnitude homology proved in
Richard Hepworth and Simon Willerton, Categorifying the magnitude of a graph, Homology, Homotopy and Applications 19(2) (2017), 31–60
and
Tom Leinster and Michael Shulman, Magnitude homology of enriched categories and metric spaces, Algebraic & Geometric Topology 21 (2021), no. 5, 2175–2221
continue to be valid for eulerian magnitude homology. In particular, with this new definition, and are still counting the number of vertices and edges in a graph respectively, since the generators of the groups and already satisfy the condition of not revisiting vertices.
Now, in order to account for the elements in containing the repetition of at least a vertex we define the discriminant magnitude chain as the quotient between the standard magnitude chain and the eulerian one.
Definition (Discriminant magnitude chain) Let be a graph. We define the discriminant -magnitude chain as
Denoting by the equivalence classes in , we define the differential map as
Splitting result
(Section removed following the conversation below.)
Applications
Subgraph counting
The example above suggests the presence of the relation we were looking for between the subgraph counting problem and the ranks of magnitude homology groups.
Lemma Let be the number of basis elements such that . The number of triangles occurring in is .
Proof Consider a -tuple of length 2. Since has length 2, and are edges of . Also, if and only if the shortest path between and has length smaller than , that is, if removing the required vertex we obtain a 2-tuple of length 1. This implies that there exists and edge and thus a triangle with vertices .
It is immediate to see that the above holds for all permutations of , which implies that the number of triangles occurring in is given by the number of basis elements in the kernel of divided by the cardinality of the automorphisms group of the triangle .
Proceeding in a similar way, it also possible to show that the number of -cliques in is upper bounded by , where is the number of basis elements such that .
A vanishing threshold for the first diagonal of EMH of Erdős–Rényi random graphs
Let be an Erdős–Rényi graph.
In order to produce a vanishing threshold for we identify what kind of subgraph is induced by a cycle in and give an estimate for the occurrences of in .
Take . Then for every the edge exists and the induced subgraph is as shown:
Now, the number of edges contained in such graph is (black edges plus blue edges). Hence, calling the number of automorphisms of , the number of copies of expected in is
The computation above implies the following.
Lemma Let be an Erdős–Rényi graph on vertices and set . The first diagonal of the eulerian magnitude homology vanishes for .
Re: Eulerian Magnitude Homology
Thanks for this post, Giulia!
Sorry about the various formatting glitches when I posted this earlier today. Hopefully they’re all fixed now, but I trust people will let me know if I missed any.
Let me see if I can start to get to grips with this.
When we look at the magnitude homology of a graph, we usually take the -chains to be generated by -tuples of vertices where
In other words, is never equal to — but it could, for instance, be equal to .
You go further, and look at the -tuples where all the are distinct. No backtracking allowed! And these are the Eulerian magnitude chains.
Of course, there are far fewer Eulerian chains than ordinary ones, because the nondegeneracy condition is more stringent. So that should make computations easier.
You then measure the difference between the ordinary and Eulerian magnitude chains, or more exactly the quotient of the former by the latter. And that’s your discriminant magnitude chain group: .
The cool thing is, at the homological level, this quotient splits:
It’s clear from the definitions that is going to be a quotient of , but the fact that it’s a direct summand is less obvious.
The upshot is that we can split magnitude homology into two parts: essentially, those generated by the chains containing no repeats, and those containing some repeats. This is nice!
That’s just about the splitting result so far, without touching the stuff on Erdős–Rényi graphs…