Random walk restart

[ArpitaHanjagi]

Algorithm: Random Walk with Restart (RWR)

Purpose: Computes relevance scores of nodes in a graph based on a random walker that restarts at a source node with a fixed probability.

Theory:

Represents graph as adjacency matrix.

At each step, walker either moves to a neighbor or restarts at source.

Iterates until probability vector converges.

Time Complexity: O(max_iter * V²) for adjacency matrix multiplication.

Space Complexity: O(V²) for adjacency matrix.

Input: Adjacency matrix, restart probability, source node, max iterations, tolerance.

Output: Vector of steady-state probabilities representing node relevance.

[Copilot]

Pull Request Overview

This pull request implements the Random Walk with Restart (RWR) algorithm, a graph-based ranking method that computes node relevance scores by simulating a random walker that probabilistically restarts at a source node.

Key Changes:

• Adds RWR algorithm implementation in random_walk_restart.r
• Includes multiple graph algorithm implementations (Kruskal's MST, Dijkstra, BFS/DFS, Floyd-Warshall, etc.)
• Adds dynamic programming solutions (Knapsack, LCS, LIS, etc.)
• Includes documentation files for machine learning algorithms

Reviewed Changes

Copilot reviewed 142 out of 222 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
graph_algorithms/random_walk_restart.r	Implements RWR algorithm with example usage
graph_algorithms/kruskal_mst.r	Kruskal's MST with Union-Find structure
graph_algorithms/dijkstra_shortest_path.r	Dijkstra's algorithm with priority queue
graph_algorithms/floyd_warshall.r	All-pairs shortest path using R6 class
graph_algorithms/breadth_first_search.r	BFS implementation with path finding
graph_algorithms/depth_first_search.r	Recursive and iterative DFS
dynamic_programming/longest_increasing_subsequence.r	O(n²) and O(n log n) LIS solutions
dynamic_programming/0/1_knapsack_problem.r	Classic 0/1 Knapsack DP solution
documentation/*.md	Machine learning documentation files
machine_learning/README.md	Tutorial links for ML in R

[siriak]

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