Vf2 graph isomorphism

[ArpitaHanjagi]

Determines whether two graphs are isomorphic (structurally identical).

Uses the VF2 algorithm, which is a backtracking approach with feasibility checks.

Checks both node compatibility and edge compatibility for already-mapped nodes.

Returns TRUE/FALSE indicating isomorphism, and a valid node mapping if they are isomorphic.

Input: Two graphs represented as adjacency lists.

Output: Boolean for isomorphism and mapping vector.

Time Complexity: Exponential in worst case, but efficient pruning reduces search space.

Space Complexity: O(n) for mapping and used arrays, where n is the number of nodes.

[Copilot]

Pull Request Overview

This pull request adds the VF2 graph isomorphism algorithm implementation to the R algorithms repository, along with several supporting graph algorithms and dynamic programming solutions. The PR introduces a comprehensive implementation of graph isomorphism detection using the VF2 backtracking approach with feasibility checks for both node and edge compatibility.

Key Changes:

• Implemented VF2 graph isomorphism algorithm with backtracking and feasibility checks
• Added supporting graph algorithms (Kruskal's MST, topological sort, various shortest path algorithms)
• Included dynamic programming implementations (knapsack, LCS, subset sum, matrix chain multiplication)

Reviewed Changes

Copilot reviewed 143 out of 223 changed files in this pull request and generated 6 comments.

Show a summary per file

File	Description
graph_algorithms/vf2_graph_isomorphism.r	Core VF2 algorithm implementation for detecting graph isomorphism
graph_algorithms/kruskal_mst.r	Kruskal's minimum spanning tree with union-find data structure
graph_algorithms/topological_sort.r	Kahn's algorithm for topological ordering of DAGs
graph_algorithms/yen_k_shortest_paths.r	K shortest paths algorithm with igraph dependency
kruskal_mst.r	Duplicate Kruskal MST implementation at repository root
Multiple dynamic programming files	Various DP implementations (knapsack, LCS, subset sum, etc.)
machine_learning/ files	Empty .gitignore and README with tutorial links
documentation/ files	Generated HTML/MD documentation files

[siriak]

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