Lean-HoG

A library for computational graph theory in Lean 4, with emphasis on verification of large datasets of graphs; in particular the House of Graphs (HoG).

Prerequisites

You need the following software:

• Lean 4 proof assistant: Install Lean 4 by following these instructions. When successful, you should have the executables elan (for installing and updating versions of Lean), lean itself, and lake (the Lean build system).
• (Visual Studio Code): the editor that has good Lean support.
• Node.js and npm cli.
• Python version 3, with the requests library, which you can install with pip3 install requests.

On MacOS you can use Homebrew to install Visual Studio Code and Node.js with brew install npm brew install --cask visual-studio-code

SAT solving

For using the SAT solving facilities of the library (e.g. computing Hamiltonian paths) you need the following:

• A modern SAT solver capable of producing proofs of unsatisfiability, we recommend CaDiCaL.
• A SAT proof checker, we recommend the formally verified checker cake_lpr.

Once you have installed the SAT solver and a proof checker, you should set in Lean * leanHoG.solverCmd to the location of the SAT solver executable. * leanHoG.proofCheckerCmd to the location of the SAT proof checker.

Installation

To install all the dependencies and compile Lean-HoG, run these commands from withing the Lean-HoG directory:

• elan update to make sure you have an up-to-date version of Lean,
• lake exe cache get to get a cached version of Mathlib (or else wait for it to compile),
• lake exe build_widgets to build the Javascript graph visualization widgets,
• lake build to compile Lean-HoG

Usage

The library uses Python to interact with the HoG database and process the data before it's imported in Lean. To make Lean aware of the location of your Python executable set set_option leanHoG.pythonExecutable <path-to-python>

Open the file Examples.lean to check whether the example graphs load successfully.

Downloading graphs

To download graphs from the House of Graphs (HoG) you can use the #download <graphName> <hog_id> command. It downloads the graphs with House of Graphs ID hog_id and loads it into the variable graphName.

You can check that it loaded it with #check <graphName>.

Note: To download the graph it uses an external python script. The location of the python executable is provided by the user option leanHoG.pythonExecutable.

Note: The python environment is expected to have the requests library installed.

Example

```lean

download Petersen 660

check Petersen

```

Visualization widget

Lean-HoG can visualize the imported graphs in the Lean infoview using widgets, which work by running Javascript in the Infoview. The visualization uses the cytoscape.js javascript library.

Try them out by opening the Examples.lean file and clicking on the line #show Cycle7. In the info view you should now see something like this:

Search the House of Graphs from Lean

You can query the House of Graphs database from within Lean via the command #search. To use it you have to construct a valid hog_query and enclose it into hog{ } syntax. It has the following syntax: hog_query q ::= boolean_invariant = b | numerical_invariant op x | query_formula op query_formula | ( q ) | q ∧ q | q ∨ q where b is a boolean value, x is a numerical value (Int for invariants with integral values, Float for invariants with continous values), op ::= < | <= | > | >= | = and query_formula f ::= x | numerical_invariant | f + f | f - f | f / f | f * f The list of available invariants can be found in the House of Graphs documentation. The invariants use lower camel case.

Example

```

search_hog hog{ bipartite = true ∧ (numberOfEdges = 1 ∨ numberOfVertices < 6) }

```

Should display the following in the Infoview:

Found 9 graphs satisfying given query Found solution hog_302 Found solution hog_300 Found solution hog_296 Found solution hog_294 Found solution hog_310 Found solution hog_298 Found solution hog_304 Found solution hog_19655 Found solution hog_49432

The solutions point to the relevant page on the House of Graphs for each graph. The graphs are also available in Lean, which you can check with e.g. ```lean

check hog_302

```

Search tactic

The library provides a tactic find_example, which uses the search feature to close certain goals of the form ∃ (G : Graph), P G for Boolean predicates P. The predicate P must be a conjunction of comparisons of invariants with either invariants or numbers. The supported invariants are those Lean-HoG currently implements. They include: * vertex size * edge size * minimum degree * maximum degree * number of connected components * traceable * non traceable * bipartite * non bipartite * connected * Hamiltonian

Example

lean example : ∃ (G : Graph), G.traceable ∧ G.vertexSize > 3 ∧ (G.minimumDegree < G.vertexSize / 2) := by find_example

Raw data format

The JSON file should have the following structure. json { "graph" : { "vertexSize" : <number of vertices>, "edges" : <list of edges>, }, <invariants> }

IMPORTANT: Lean-HoG expects all lists and maps, including the list of edges, to be (lexicographically) ordered. In particular, edges must be ordered pairs ([1, 2], never [2, 1]) and the list of edges should be lexicographically ordered.

Examples of JSON encoding of graphs

Consult the (examples)[./examples] folder.

Neighborhood map

json "neighborhoodMap" : { "neighbors" : <map vertices to their neighbors>, }

Connected components

json "connectedComponents" : { "val" : <number of components>, "component" : <map vertices to components>, "root" : <map components to their roots>, "next" : <map vertices to their parents, roots to roots>, "distToRoot" : <map vertices to their distance to the root>, }

Bipartite

json "bipartite" : { "color" : <map vertices to color, 0 or 1>, "vertex0" : <vertex with color 0>, "vertex1" : <vertex with color 1>, },

Odd closed walk

json "oddClosedWalk" : { "closedWalk" : <list of vertices>, }