SoroushVahidi/mwfas-heuristic-metrics

MWFAS Heuristic Metrics

MWFAS Heuristic Metrics v1 is a project-generated, metrics-only dataset (7,371 rows across 14 tables) reporting how heuristic algorithms perform on the Minimum Weighted Feedback Arc Set (MWFAS) problem: given a directed graph whose arcs each have a weight, find the minimum-weight set of arcs whose removal makes the graph acyclic. The problem is directly connected to ranking from pairwise comparisons — a weighted directed edge can represent one item being preferred over another, so minimizing the removed weight corresponds to finding an ordering with the smallest total weighted disagreement. This release covers run-level and aggregate outcomes (objective quality, runtime, robustness, and ablations) for the IPSNS/LR-TA/WMSF heuristic family and several baselines, evaluated on graph instances from the third-party alidasdan/graph-benchmarks collection and other third-party sources (not redistributed here).

This dataset does not include raw third-party graph benchmark files, graph edge lists, adjacency lists, raw ranking vectors, manuscript drafts, logs, or machine-local execution artifacts.

Canonical Hugging Face repository: SoroushVahidi/mwfas-heuristic-metrics.

One row represents: one sanitized experimental outcome — but the exact grain (a single algorithm run, a single instance-level ablation, or a pre-aggregated summary) differs by config. See the table below and the per-config descriptions further down.

Why this dataset exists

Reproducing the full MWFAS heuristic-evaluation pipeline (parameter tuning, sensitivity sweeps, stochastic robustness runs, MIP baselines, and ablations across dozens of benchmark instances) is compute-intensive. This dataset publishes the resulting run-level and aggregate metrics directly, so researchers can audit, reanalyze, or build on the reported heuristic behavior without rerunning the experiments.

What is included

• 14 Parquet configs of project-generated run-level and aggregate metrics for MWFAS heuristic experiments.
• Public-safe graph structural metadata (n_vertices/n, n_edges/m, density) per instance.
• Sanitized instance and configuration identifiers, algorithm/variant labels, seeds, and objective/runtime outcomes.

What is not included

• Raw benchmark graph files, edge lists, or adjacency matrices (third-party; not redistributed).
• Temporary ranking vectors, raw output paths, local filenames, machine names, CPU/environment details, PIDs, or process metadata.
• Manuscript drafts, reviewer/editorial material, logs, and private build-audit files.
• The separate Ranking-by-FAS/GNNRank result matrices — those are a different project, published separately as SoroushVahidi/ranking-fas-results.

Dataset size

Config	Rows	Unit of observation	Experiment family	Purpose
run_metrics	1,290	One checkpointed IPSNS parameter-grid run on one tuning or holdout graph instance	COAP holdout (stage 2)	Validate IPSNS engineering defaults on disjoint tuning/holdout splits after stage-1 screening (experiments/coap_ipsns_holdout/).
sensitivity	140	One one-at-a-time IPSNS parameter variant on one of 10 ablation instances	COAP sensitivity (stage 1)	Screen IPSNS parameter sensitivity on the EXP2 ten-instance subset (experiments/coap_ipsns_sensitivity/).
robustness	3,720	One repeated run of IPSNS (20 seeds) or DRMacIver/FAS (20 runs) on one sparse instance	Stochastic robustness (EXP10)	Test whether EXP4 sparse-comparison advantages persist under repeated runs on 93 instances (experiments/exp10_stochastic_robustness/).
aggregate_summary	96	One pre-aggregated summary row for a config/metric family	Derived aggregate	Summaries derived only from run_metrics, sensitivity, and robustness.
core_benchmark	369	One (graph instance, algorithm) outcome row	Main internal benchmark (EXP1b)	123-instance DIMACS reproducibility run: LR-TA, WMSF, and IPSNS on the core sparse benchmark (experiments/exp1b_core_benchmark_full_wmsf_seed/).
ablation	80	One (instance, algorithm_variant) run	Component ablation (EXP2)	Eight IPSNS/LR-TA/WMSF component variants on 10 representative instances (experiments/exp2_ablation/).
exact_small	67	One (small instance, method) outcome vs exact DP	Exact validation (EXP3)	Heuristic vs bitmask-DP optimum on instances with n ≤ 20 (experiments/exp3_exact_small/).
external_baselines	984	One (sparse instance, algorithm) run	External baseline comparison (EXP4)	IPSNS/LR-TA/WMSF vs external FAS heuristics on 97 standard sparse instances (experiments/exp4_external_baselines/).
lolib_dense	400	One (LOLIB dense instance, algorithm) run	Dense transfer study (EXP5)	Heuristic transfer to 50 dense LOLIB tournament instances (experiments/exp5_lolib_dense/).
budget_curve	120	One (instance, IPSNS iteration budget) run	Parameter sweep (EXP6)	IPSNS quality–runtime curve over budgets {10,25,50,100,200,400} on a 20-instance subset (experiments/exp6_ipsns_budget_curve/).
plain_local_search	60	One (instance, plain-LS method) run	Heuristic control (EXP7)	Generic order-local-search controls from LR-TA/WMSF seeds vs IPSNS (experiments/exp7_plain_local_search/).
medium_mip_baseline	15	One medium sparse instance with MIP/LP bounds vs heuristics	Solver baseline (EXP8)	Time-capped MIP/LP baseline on 15 medium instances (experiments/exp8_medium_mip_baseline/).
application_case	6	One (Wiki-Vote top-N subgraph, algorithm) outcome	Application case study (EXP9)	Real-network case study on a SNAP Wikipedia adminship vote subgraph (experiments/exp9_application_case/).
topological_extraction_sensitivity	24	One (instance, extraction_rule) post-hoc summary	Extraction sensitivity (EXP11)	Post-hoc comparison of four topological extraction rules on 6 calibration instances (experiments/exp11_topological_extraction_sensitivity/).
Total	7,371

Source repository: minimum-weighted-fas-heuristics (docs/EXPERIMENT_REGISTRY.csv maps each experiment ID to these directories).

Choosing a config

If your question is…	Start with…
How do LR-TA, WMSF, and IPSNS compare on the main sparse graph benchmark?	core_benchmark
How does IPSNS compare to strong external FAS heuristics?	external_baselines
Are EXP4-style sparse wins robust to random seeds / restarts?	robustness
Which IPSNS parameters matter, and do tuned defaults hold out-of-sample?	sensitivity, then run_metrics
Which IPSNS components (add-back, SCC priority, iteration count) matter?	ablation
Where does IPSNS quality saturate vs iteration budget?	budget_curve
Can generic local search match IPSNS gains?	plain_local_search
How close are heuristics to exact optima on tiny instances?	exact_small
How close are heuristics to MIP/LP bounds on medium instances?	medium_mip_baseline
How do methods behave on dense LOLIB instances?	lolib_dense
Does the framework apply to a real network ranking case?	application_case
How sensitive are reported objectives to topological extraction rules?	topological_extraction_sensitivity
Pre-aggregated views of the COAP/robustness primary configs?	aggregate_summary

For ranking from pairwise comparisons with GNNRank/classical ranking baselines, use SoroushVahidi/ranking-fas-results instead — it measures ranking upset/violation rates on tournament-style judgment data, not MWFAS backward-weight objectives on DIMACS graphs.

Problem background

A feedback arc set is a set of directed arcs whose removal makes a directed graph acyclic. In the weighted version, each arc has a weight, and the objective is to minimize the total weight of arcs that point backward under the produced ordering — lower is better. This connects to ranking from pairwise comparisons: a weighted directed edge can represent one item being preferred over another, and minimizing backward weight corresponds to finding an ordering with small weighted disagreement.

Important fields

• benchmark_resource: upstream benchmark collection referenced by the row. Raw benchmark files are not redistributed here.
• instance_family: sanitized family/group inferred from the source benchmark path (e.g. core, core-bad, iscas).
• instance_id: public benchmark-local instance identifier — not an edge list or graph encoding.
• n_vertices, n_edges (or n, m in some configs): graph structural metadata.
• density: directed density, n_edges / (n_vertices * (n_vertices - 1)), when applicable.
• algorithm / algorithm_variant: heuristic or baseline identifier, e.g. IPSNS or DRMacIver/FAS.
• objective_weight (or backward_weight/total_weight in some configs): weighted backward-arc objective; lower is better.
• normalized_objective: objective divided by total edge weight, when available.
• runtime_seconds (or runtime): measured runtime in seconds — implementation/environment-dependent, not a hardware-independent complexity measure.
• improvement_absolute / improvement_relative: improvement over the initial incumbent objective, where available.
• validated: boolean validation flag derived from source status and (for robustness rows) ordering/objective/acyclicity checks.
• source_commit: source repository commit associated with the record, where recoverable.

Full per-config column lists are in metadata/schema.json in the dataset repository.

Quickstart

from datasets import load_dataset

# Each table is a separate config — pick the one you need:
ds = load_dataset("SoroushVahidi/mwfas-heuristic-metrics", "run_metrics")
print(ds)

# Other available configs: "sensitivity", "robustness", "aggregate_summary",
# "core_benchmark", "ablation", "exact_small", "external_baselines", "lolib_dense",
# "budget_curve", "plain_local_search", "medium_mip_baseline", "application_case",
# "topological_extraction_sensitivity"

This dataset is small (7,371 rows total across all configs); no streaming is needed.

Research use cases

• Reproducing and auditing MWFAS heuristic experiment summaries without rerunning them.
• Analyzing runtime/objective tradeoffs across graph instances and configurations.
• Studying parameter sensitivity (sensitivity, topological_extraction_sensitivity, budget_curve) and stochastic robustness (robustness).
• Comparing heuristic objectives against exact-solver or MIP bounds where available (exact_small, medium_mip_baseline).
• Meta-analysis or meta-learning over algorithm outcomes using structural graph metadata as features.

Non-intended uses

• A replacement for the upstream graph benchmark datasets.
• A source of graph edge lists or raw ranking data.
• A universal benchmark of all FAS/MWFAS algorithms.
• A hardware-independent runtime leaderboard.
• Evidence that one method dominates outside the stated benchmark families and protocols.

Generation methodology

Results are drawn from git-tracked experiment directories in the minimum-weighted-fas-heuristics source repository (build reference commit 40209c26966247d9bf9ad34764de4ac4181f98c2; see docs/EXPERIMENT_REGISTRY.csv):

Config	Source path
run_metrics	experiments/coap_ipsns_holdout/results/runs.jsonl
sensitivity	experiments/coap_ipsns_sensitivity/summary/canonical_runs.csv
robustness	experiments/exp10_stochastic_robustness/summary/run_level_results.csv
core_benchmark	results/tables/unified_reproducibility_summary.csv (123-instance DIMACS reproducibility run)
ablation	experiments/exp2_ablation/tables/exp2_ablation_summary.csv
exact_small	experiments/exp3_exact_small/tables/exp3_exact_summary.csv
external_baselines	experiments/exp4_external_baselines/tables/exp4_external_paper_summary.csv
lolib_dense	experiments/exp5_lolib_dense/tables/exp5_lolib_paper_summary.csv
budget_curve	experiments/exp6_ipsns_budget_curve/summary/
plain_local_search	experiments/exp7_plain_local_search/summary/
medium_mip_baseline	experiments/exp8_medium_mip_baseline/summary/
application_case	experiments/exp9_application_case/summary/
topological_extraction_sensitivity	experiments/exp11_topological_extraction_sensitivity/summary/exp11_per_instance.csv
aggregate_summary	Derived only from run_metrics, sensitivity, and robustness.

Provenance and ownership

Artifact	Classification
All 14 published Parquet configs	Created/generated by Soroush Vahidi (derived run-level metrics from own experiments)
alidasdan/graph-benchmarks instances (referenced by instance_id/benchmark_resource)	Third-party upstream data; not redistributed
LOLIB dense instances (lolib_dense config)	Derived from third-party source data; raw LOLIB files not redistributed

Author: Soroush Vahidi, New Jersey Institute of Technology. ORCID: 0000-0003-1934-6282 (from this project's own CITATION.cff).

Relationship to related datasets

This is a graph-optimization / weighted-feedback-arc-set experimental metrics dataset. It is distinct from SoroushVahidi/lafc-evict (cache-eviction candidate supervision), SoroushVahidi/module-intervention-credit and SoroushVahidi/llm-serving-scheduler-baselines (LLM-serving scheduler data), SoroushVahidi/consistency-aware-judgments (IR pairwise LLM judgments), SoroushVahidi/frontier-allocation-metrics (budgeted LLM inference outcomes), SoroushVahidi/scidocs (a third-party BEIR mirror), and SoroushVahidi/lafc-evict-sample (a synthetic workflow artifact).

Most directly related — and most easily confused with — SoroushVahidi/ranking-fas-results:

	mwfas-heuristic-metrics (this dataset)	ranking-fas-results
Problem framing	Minimum Weighted Feedback Arc Set on general weighted digraphs	Ranking from pairwise comparisons
Benchmark ecosystem	alidasdan/graph-benchmarks DIMACS-style directed weighted graphs, LOLIB, others	GNNRank pairwise-comparison benchmarks (tournament-style data)
Methods measured	IPSNS / LR-TA / WMSF / DRMacIver-FAS heuristics, plus MIP/exact baselines	OURS_MFAS-family vs. classical ranking baselines vs. GNNRank neural methods
Primary metric	Feedback-arc-set objective weight and runtime	Ranking "upset"/violation rate
Row granularity	One heuristic algorithm run on one graph instance (varies by config)	One (dataset × method × config) comparison result

Neither dataset supersedes the other; there is no row-level overlap.

Limitations

• Runtime values depend on implementation and execution environment.
• The released metrics are historical results from specific code/protocol versions.
• run_metrics and sensitivity emphasize IPSNS parameter behavior, not a full cross-algorithm benchmark.
• robustness is quality-focused, not an equal-time comparison.
• Raw graph inputs are excluded; users must fetch upstream graph benchmarks separately to rerun algorithms from scratch.
• Subset configs (ablation, budget_curve, exact_small, medium_mip_baseline, application_case, topological_extraction_sensitivity) use smaller instance sets by design; do not treat them as full-benchmark substitutes for core_benchmark or external_baselines.

Version history

• v1 (current), immutable data revision 5621684d3c03138d5b2ebe544e91ae5698e67b5a; repository metadata HEAD 653b415dcf53281730fd476c5307a371d1a792e2 (2026-08-18). This revision already supersedes an earlier 4-config baseline (2caabbe...) via 10 additive new configs — no further versioning action needed beyond the task_categories metadata fix documented here.

Related resources

• Source code repository: https://github.com/SoroushVahidi/minimum-weighted-fas-heuristics
• Primary associated paper: Soroush Vahidi and Ioannis Koutis, "Minimum Weighted Feedback Arc Sets for Ranking from Pairwise Comparisons," arXiv:2412.16181.
• Related supporting work (IPSNS/SCC-neighborhood component, not the primary dataset citation): Soroush Vahidi, "Incumbent-Protected SCC-Neighborhood Search for the Weighted Feedback Arc Set Problem," SSRN abstract 6281222.
• Related Hugging Face dataset: SoroushVahidi/ranking-fas-results (see "Relationship to related datasets" above).

Citation

Cite the dataset when using the released metrics. Cite the paper when discussing the methodology or scientific findings. Cite both when using the data and materially relying on the associated methodology/results.

Vahidi, S. (2026). MWFAS Heuristic Metrics: Run-Level Outcomes for Minimum Weighted Feedback Arc Set Experiments (v1) [Data set]. Hugging Face. https://huggingface.co/datasets/SoroushVahidi/mwfas-heuristic-metrics

@dataset{vahidi2026mwfasheuristicmetrics,
  title        = {MWFAS Heuristic Metrics: Run-Level Outcomes for Minimum Weighted Feedback Arc Set Experiments},
  author       = {Vahidi, Soroush},
  year         = {2026},
  version      = {v1},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/SoroushVahidi/mwfas-heuristic-metrics},
  note         = {Derived metrics dataset; no dataset DOI assigned}
}

@article{vahidi2024mwfas,
  title   = {Minimum Weighted Feedback Arc Sets for Ranking from Pairwise Comparisons},
  author  = {Vahidi, Soroush and Koutis, Ioannis},
  journal = {arXiv preprint arXiv:2412.16181},
  year    = {2024},
  doi     = {10.48550/arXiv.2412.16181}
}

No dataset DOI has been assigned. For exact reproducibility, cite the immutable Hugging Face revision 5621684d3c03138d5b2ebe544e91ae5698e67b5a.

License

CC BY 4.0 for the released project-generated metrics, summaries, metadata, and documentation, to the extent controlled by this project. This does not relicense upstream graph benchmark datasets, third-party software, or papers referenced by identifier or citation.