Skills Data Science ICDM Research Reproducibility Guidelines

ICDM Research Reproducibility Guidelines

v20260724
icdm-reproducibility
A comprehensive guide for ensuring the reproducibility of data mining research for top-tier conferences like ICDM. It addresses the unique challenges of triple-blind review and strict page limits by detailing necessary artifacts, such as configuration files, seed lists, and anonymized packages. Learn how to achieve Rerunnable, Rebuildable, or Attested reproducibility tiers to solidify your scientific claims.
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Overview

ICDM Reproducibility

Make the mining result reproducible as science, under two ICDM-specific pressures: the Research Track is triple-blind, so the reproduction package cannot reveal identity, and the reporting all competes for space inside the 10-page all-inclusive cap. Reproducibility at ICDM is not a separate checklist form (verify per edition); it is evidence that the discovery is real rather than a lucky configuration.

What must be reproducible

  • The mining task setup: exact datasets, versions, preprocessing, splits, and how ground truth or injections were generated.
  • The method: hyperparameters, model selection procedure, the mechanism's knobs (e.g. sketch size, partition count), and the random seeds.
  • The evaluation: metrics, thresholds/cutoffs, variance estimation, and the hardware behind any timing claim.

Reproducibility tiers

Tier What a reviewer can do How to reach it
Rerunnable Re-execute your scripts and get your tables Pinned deps, seeds, config files, entry script
Rebuildable Reconstruct the pipeline from description alone Precise protocol in the paper + appendix
Attested Trust numbers that cannot be shared (private data) Documented protocol + synthetic proxy + honest scope

Aim for rerunnable on any public-data result; use attested only where data genuinely cannot be released, and say so plainly.

Configs as artifacts

Put the run behind a config, not scattered command-line flags, so a reviewer reproduces a table by pointing at a file.

# repro/config.yaml  (anonymized; no author paths, no institutional dataset names)
task: stream_anomaly_ranking
dataset: public_edge_stream_v3      # public source + version, not an internal name
seeds: [0, 1, 2, 3, 4, ... , 19]    # the 20 seeds behind the reported variance
method:
  sketch_partitions: 128            # the mechanism knob mapped in the ablation
  hash_family: multiplicative
eval:
  metric: precision_at_k
  k: 100
  time_respecting_split: true
compute:
  hardware: "1x consumer GPU, 16GB"  # generic; states the basis of any timing claim

Triple-blind the package

  • Export a fresh repository with no git history — commit metadata routinely leaks author names and institutions.
  • Remove author paths, usernames, internal dataset names, cluster hostnames, and README acknowledgements.
  • Host it so the link in the PDF resolves to an anonymized location, not a named account.
  • Because ICDM traditionally offers no rebuttal, this package is often the only extra evidence a reviewer ever sees — it must be complete and anonymous at submission time.

Report inside the page cap

  • The full reproduction protocol can live in an in-cap appendix and the cited repository; the body must still state seeds, key hyperparameters, and the compute basis of timing claims.
  • Prefer a compact reproducibility paragraph plus a repository over a sprawling appendix that eats pages the body needs.

Vignette: the seed table that answered a review before it was written

A team worried reviewers would read their close margins as noise. Rather than hope for a rebuttal that ICDM might not offer, they reported every metric with a standard deviation over 20 seeds, shipped the seed list and configs in an anonymized, history-scrubbed repository cited in the PDF, and stated the exact GPU behind their latency numbers. The "is this just noise?" review never came, because the paper had already answered it — and nothing in the package revealed who they were.

Output format

[Repro tier] rerunnable / rebuildable / attested (per result)
[Seeds+variance] reported: yes / no
[Config-as-artifact] present: yes / no
[Anonymized package] history-scrubbed + no institutional names: yes / leaks found
[Compute basis] hardware behind timing claims stated: yes / no
[Top gap] <single most important missing reproducibility detail>
Info
Category Data Science
Name icdm-reproducibility
Version v20260724
Size 4.21KB
Updated At 2026-07-28
Language