Rewrite scalefree degree generation with truncated discrete power law#73
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Rewrite scalefree degree generation with truncated discrete power law#73
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Replace continuous Pareto sampler with proper discrete power-law sampling:
- Implement truncated discrete power law P(K=k) ∝ k^(-gamma) on k ∈ {kmin..kmax}
- Solve for gamma numerically via Brent's method to match target average_degree
- Sample in-degrees and out-degrees separately (not split from total)
- Balance sums with vectorized integer-only adjustments (bincount)
- Validate kmax <= n-1 for simple directed graphs (no self-loops/multi-edges)
- Replace asserts with explicit ValueError for production safety
- Add comprehensive unit tests (40 tests for new functions)
- Update configs to use kmin parameter instead of loc
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Replace continuous Pareto sampler with proper discrete power-law sampling: