Optimization: iFUB algorithm for unweighted graph diameter

[LoveLow-Global]

Implement iFUB algorithm for diameter calculation of unweighted graphs, SimpleGraph and SimpleDiGraph

Below is a comparison with the original diameter calculation logic, ran on my desktop.

using Graphs

g = erdos_renyi(10000, 0.005) # 10k nodes, sparse

# New implentation
@time d = diameter(g) # 0.910222 seconds (372.05 k allocations: 6.602 MiB)

# Original Method
original_diameter(g) = maximum(eccentricity(g)) 
@time original_diameter(g) # 41.471079 seconds (650.01 k allocations: 13.515 GiB, 3.59% gc time)

[codecov]

Codecov Report

❌ Patch coverage is 98.52941% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 97.17%. Comparing base (4176651) to head (67d7344).

Files with missing lines	Patch %	Lines
src/distance.jl	98.52%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #482      +/-   ##
==========================================
+ Coverage   97.16%   97.17%   +0.01%     
==========================================
  Files         121      121              
  Lines        7020     7088      +68     
==========================================
+ Hits         6821     6888      +67     
- Misses        199      200       +1     

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[Krastanov]

Thank you! On first look this looks great, but it will take a bit to get a proper review.

In the meantime, we should probably have some more rigorous tests for it added. (1) Tests comparing against large graphs with known diameters (e.g. by construction); and (2) tests comparing against the old slow implementation for random graphs. Could you add such tests, maybe something that adds an additional 60 seconds to the tests, but with a "number of random samples" parameter that I can change locally and run for a few hours on my machine?

[LoveLow-Global]

Hello Krastanov, thanks for the comment. I have added (1) comparison against graphs with known diameters and the (2) comparison with old slow implementation for random graphs, under test/distance.jl. You can change NUM_SAMPLES there. Also, please note that it is my first time contributing to an existing Julia package, so please tell me if there is any changes to be done.

[Krastanov]

Thanks, this looks great! I will try to review this in the next few days.

One other thing that I need to check lately -- if you used an LLM-like tool, could you disclose that, which tool, and what the prompt was? I do not see anything in your PR that makes me think that was the case, and it is not like LLM tools are bad (they help a lot), but they do require approaching the review differently and having this extra info helps.

[LoveLow-Global]

Thanks for the feedback! I have used Gemini 3.0 for code generation. This optimization method was originally used in one of my researches, but I needed to change the style of the code to be similar to Graphs.jl. Therefore, I used Gemini to first change the code into a "Graphs.jl" format. Then I manually edited the codes again.

[Krastanov]

Do you happen to have a reference of where this algorithm was introduced first / who named it iFUB, etc? Or a textbook about it?

[Krastanov]

Pardon the formatter error -- it seems it is due to an incorrect security configuration on our end, not due to your code.