"""
Graph algorithm implementations for ParquetFrame.

This module provides efficient graph traversal and analysis algorithms with
intelligent pandas/Dask backend selection. All algorithms work with GraphFrame
objects and return structured DataFrames with consistent column schemas.

Core Algorithms:
    bfs: Breadth-First Search with multi-source support
    dfs: Depth-First Search with discovery/finish times
    shortest_path: Unweighted (BFS) and weighted (Dijkstra) shortest paths
    connected_components: Weakly connected components
    pagerank: PageRank with personalization support

Examples:
    Basic traversal:
        >>> from parquetframe.graph.algo import bfs, dfs
        >>> graph = pf.read_graph("social_network/")
        >>> result = bfs(graph, sources=[1, 2, 3], max_depth=5)
        >>> print(result[['vertex', 'distance', 'predecessor']])

    Shortest paths:
        >>> from parquetframe.graph.algo import shortest_path
        >>> paths = shortest_path(graph, sources=[1], weight_column="distance")
        >>> print(paths.query("distance < float('inf')"))

    PageRank analysis:
        >>> from parquetframe.graph.algo import pagerank
        >>> ranks = pagerank(graph, alpha=0.85, max_iter=100)
        >>> top_vertices = ranks.nlargest(10, 'rank')
"""

# TODO: Phase 1.2 - Import algorithm functions as they are implemented
# from .traversal import bfs, dfs
# from .shortest_path import shortest_path
# from .components import connected_components
# from .pagerank import pagerank

# Placeholder exports for development - will be uncommented as algorithms are implemented
__all__ = [
    # "bfs",
    # "dfs",
    # "shortest_path",
    # "connected_components",
    # "pagerank",
]

# Version info for algorithm module
__version__ = "1.2.0"
__author__ = "ParquetFrame Contributors"
