How to measure the similarity between two graph networks?

I have a set of undirected graph networks, 6 nodes each with weighted edges. I would like to compare each with a reference graph network which also has the same 6 nodes but with different weights. What is a good method to compare them and have a similarity score, say between zero to one?

UPDATE: To clarify my problem, I am going to explain what is the problem I am looking at. I have a series of protein structures which are identical in their amino acid sequence (same nodes). However they are slightly different in their spatial orientation. I want to see which amino acids interact together (if two amino acids are within a certain cutoff distance, then it would mean they are interacting -- an edge between the two nodes). They may be so close so the edge weight would be 1 or less until it reaches zero (weights of edges). I want to see which protein structures (graph networks) are similar in the way their amino acids interact?

• When you say "the same nodes", do you mean that they have edges in the same place as well? i.e. visually the same network, but with different weights on the edges?
– πr8
Jan 7 '17 at 18:12
• @πr8 Not necessarily the same edges. I just said that because I wanted to emphasize that the nodes represent the same things. However, I would like to know the answer for both cases (with the same edges or without).
– Wise
Jan 7 '17 at 18:17
• Right. I'll say ahead of time that finding "best method" is unlikely to be well-defined, at least without further information. One approach that might be useful is to consider adjacency matrices, and using this to construct some metric.
– πr8
Jan 7 '17 at 18:20
• Alright I changed "best method" to "a good method".
– Wise
Jan 7 '17 at 18:23
• Right, but there's still very real questions of what that means - good for what?
– πr8
Jan 7 '17 at 18:24

Here is one idea. Let $G_1$ and $G_2$ be different graphs on the same vertices, let $L_1$ and $L_2$ be their graph Laplacians, and let the superscript plus symbol denote the pseudoinverse (e.g., $A^+$ is the pseudoinverse of $A$). Then one can define something like:

$$\text{similarity}(G_1, G_2) := \exp \left(-\frac{||L_1^+ - L_2^+||^2}{||L_1^+||~||L_2^+||}\right).$$

The norm $||\cdot||$ can be any matrix norm. I would recommend, for example, the Frobenius norm. Another good choice could be the induced $2$-norm.

Anyways, the idea is that the graph Laplacian is related to diffusion (e.g., of randomly moving particles, or heat) in the graph, in that the $i$'th column of the psdueoinverse matrix $L^+$ is the steady-state result of putting a constant source of particles (or heat, etc) at node $i$ and letting them diffuse randomly through the graph, with the probability of transmission between two nodes related to the edge weight between those nodes. So, $||L_1^+ - L_2^+||$ measures the difference in the graphs in terms of how they physically differ in a diffusion process.

Then dividing by $||L_1^+||~||L_2^+||$ is done to make the similarity measure scale-invariant, and the exponential $\exp\left(-\dots\right)$ is taken to map the values between $0$ and $1$.

This is just my crazy idea so take it with a grain of salt. I don't know if anyone else has done this already; probably someone has.

• Hi, could you please provide any reference (book or research paper) on this. Nov 11 '19 at 12:57
• @Saikat Dunno, I just thought of this when I answered the question. Not sure if anyone else has done it. Nov 11 '19 at 15:57
• Ok, thanks Nick. Nov 11 '19 at 17:17