Kdd Artifact Evaluation

KDD Artifact Evaluation

Use this while the submission is being assembled — not after. KDD's review mechanics create one hard constraint that reorders all artifact work: the rebuttal phase does not allow hyperlinks, so the anonymized repository referenced inside the submitted PDF is the complete and final artifact channel for the whole review. There is no "we'll share code if reviewers ask"; asking happens in a phase where you cannot answer with a link.

Artifact strategy by track

Building the anonymized repository

• Create a fresh repository, never a scrubbed clone: git history, CI configs, issue templates, and .git metadata leak identity that no README edit removes.
• Sweep for: usernames in paths, institutional cluster names, cloud bucket names, internal package registries, license headers, notebook execution metadata, and dataset names that only one company uses.
• Cite the repository in the paper body (and optionally the abstract); after the deadline this reference is unchangeable, so double-check the URL resolves logged out.
• Structure for a 10-minute reviewer, because that is the realistic inspection budget:

anonymous-artifact/
├── README.md            # 1 screen: claim -> command -> expected output table
├── env/                 # lockfile or container spec, exact versions
├── configs/             # one config per reported table/figure row
├── data/
│   ├── get_data.sh      # public downloads, checksums
│   └── sample/          # small slice so the pipeline runs in minutes
├── run.sh               # regenerates the smallest headline result end-to-end
└── results/expected/    # committed reference outputs for diffing
# smoke-test on a clean machine:
docker run --rm -v $PWD:/w -w /w python:3.11 bash -c "pip install -r env/requirements.txt && bash run.sh --sample"

Scale claims need scale artifacts

KDD reviewers read "scales to billions of edges" as a checkable claim, not marketing:

• Ship the throughput/memory harness, not only accuracy scripts; a scalability figure without its benchmark script is the least-trusted plot in the paper.
• Record hardware, dataset cardinalities (rows/edges/events), and wall-clock per run in a machine-readable manifest, so the repo answers "on what?" precisely.
• Where the full-scale run needs a cluster no reviewer has, provide the mid-scale run that completes on one machine plus the exact extrapolation logic the paper uses.

ADS evidence packaging without leaking production

The ADS track requires quantified post-launch performance, but production data almost never ships. Reviewers accept that trade when the package contains:

• Exact metric definitions (numerator, denominator, unit, aggregation window) for every post-launch number in the paper.
• The measurement design: A/B split or pre/post window, traffic share, duration, and what guardrail metrics were tracked.
• A synthetic or replayed data generator that exercises the same pipeline shape, so the method is runnable even though the evidence is observational.
• An honesty note distinguishing measured production numbers from simulated ones — mixing them silently is a rejection pattern.

After acceptance

• Replace the anonymous mirror with the public, licensed, citable repository; the camera-ready must carry the durable link (see kdd-camera-ready).
• Prefer an archival deposit (DOI-stamped release) over a bare git URL for the version that the proceedings reference.
• Check the current cycle for artifact-badging or code-availability initiatives before assuming none exists (待核实 — such programs change year to year).

Vignette: packaging a billion-edge graph paper

A Research Track paper claims its sampler trains GNNs on a 3B-edge graph on one machine. What the artifact must contain for that claim to survive contact with a skeptical reviewer:

• get_data.sh that downloads the public 3B-edge graph (or constructs it from public parts) with checksums — a scale claim on an unfetchable graph is attested, not rerunnable, and should be labeled accordingly (kdd-reproducibility).
• A --scale small|medium|full switch: small finishes on a laptop in minutes and validates the pipeline; medium reproduces one main-table row on a single GPU overnight; full documents the exact hardware used for the headline.
• The peak-memory tracker wired into every run, because the paper's "one machine" claim is really a memory claim.
• results/expected/ with per-scale reference outputs, so a reviewer's partial rerun has something to diff against.

What it must not contain: the 40GB of intermediate artifacts (regenerable), the authors' cluster submission scripts (identity leak), or a README promising "full instructions after acceptance" — that sentence tells reviewers the artifact is theater.

Common artifact failures at this venue

Output format

[Artifact channel] repo cited in PDF: yes/no (if no: unrecoverable after deadline)
[Track register] research-repro / ads-deployment-evidence / dataset-release
[Regeneration level] one-command sample / scripted / descriptive only
[Scale evidence] throughput+memory harness: present / missing
[Anonymity sweep] <paths/history/metadata findings>
[Post-acceptance plan] <public repo, license, archival DOI>

Source: brycewang-stanford/Awesome-Journal-Skills → KDD-Skills/skills/kdd-artifact-evaluation/SKILL.md