Add structured optimization-target diagnostics for centrality, null models, and uncertainty workflows

[Copilot]

This PR addresses the request to identify likely performance bottlenecks in centrality, null-model, and uncertainty paths, and expose concrete optimization targets in code (without adding new markdown docs).
It adds a programmatic diagnostics surface that returns scan-friendly, machine-readable bottleneck recommendations tied to specific functions and code locations.

• New diagnostics API for performance targeting

  • Added py3plex/diagnostics/optimization_targets.py with:
    • OptimizationArea (centrality, null_models, uncertainty)
    • OptimizationTarget (function/file/line range, bottleneck, complexity, optimization target, expected gain, impact)
    • OptimizationTargetReport (targets + network context + optional profiling)
    • find_optimization_targets(...) entrypoint for filtered, structured reports

• Concrete hotspot catalog (high-signal, code-addressable)

  • Encodes likely bottlenecks and optimization targets for:
    • centrality robustness recomputation / graph reconstruction loops
    • null-model multiprocessing serialization and per-edge rebuild overhead
    • uncertainty co-association / VI/NMI nested-loop paths and serial sampling loops
  • Each target includes precise function path and line range to support direct follow-up optimization work.

• Runtime context integration

  • Report includes lightweight network-size context (nodes, edges, layers).
  • Optional integration with global PerformanceMonitor to attach observed timing stats for matching hotspot functions.

• Public package export

  • Exposed new API from py3plex.diagnostics (find_optimization_targets, OptimizationArea, OptimizationTarget, OptimizationTargetReport) for direct use by tooling and downstream workflows.

• Focused coverage for new behavior

  • Added tests/test_diagnostics_optimization_targets.py to validate:
    • area coverage and filtering
    • network stats extraction
    • profiling attachment shape
    • report serialization contract

from py3plex.diagnostics import find_optimization_targets, OptimizationArea

report = find_optimization_targets(
    network=net,
    areas=[OptimizationArea.CENTRALITY, OptimizationArea.UNCERTAINTY],
    include_profiling=True,
)

for t in report.targets:
    print(t.function, t.line_range, "->", t.optimization_target)

payload = report.to_dict()  # machine-readable for CLI/UI/automation

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