Graph Peripherality - #11898

Title: Add graph peripherality algorithms for computing peripheral and far nodes

Description:

This pull request introduces new algorithms to compute the peripheral and far nodes in a weighted graph. It includes the implementation of the Floyd-Warshall algorithm for all-pairs shortest paths and functions to calculate eccentricity and harmonic closeness centrality. The code is well-documented, includes comprehensive type hints, and contains doctests for various graph scenarios to ensure correctness.

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

Commit:

Initial Commit: Add algorithms to determine peripheral and farthest nodes in a weighted graph

• Implemented Floyd-Warshall algorithm to calculate all-pairs shortest paths,
  supporting positive edge weights for weighted graphs.
• Added functions to identify peripheral and far nodes:
  • find_peripheral_node: Determines the node with maximal eccentricity.
  • find_far_node: Finds the node with maximal farness, summing shortest-path distances.
• Included error handling for non-positive edge weights, with appropriate ValueError raising.
• Developed extensive test cases using doctests:
  • Covered single-node, fully connected, sparse, cyclic, directed acyclic, and
    disconnected graphs.
  • Validated handling of edge cases including zero and negative weights.
• Ensured code meets PEP 8 standards and passed all lint checks with ruff.
• Provided detailed module-level docstring with explanations of algorithms,
  complexity, and example applications.