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name: imc-related-work description: Use when writing or auditing the related-work and positioning of an ACM IMC paper, covering the measurement literature lanes, positioning against prior datasets and vantage points, delta-first framing, citing the datasets and tools you build on, distinguishing IMC from SIGCOMM/NSDI/PAM prior art, and keeping self-citations double-blind.

IMC Related Work

Use this to position an IMC paper. In measurement, related work is not just prior claims — it is prior datasets, vantage points, tools, and methodologies. IMC reviewers know the measurement landscape intimately; a positioning that ignores the dataset or platform your finding builds on, or that misattributes measurement work to the wrong venue, reads as unfamiliarity with the field.

Cover the measurement literature lanes

Position against the relevant lanes, not a generic "prior work" pile:

• The same phenomenon, measured before: who measured this, from which vantage points, when, and what did they find? Your delta is usually breadth, recency, a better vantage point, or a correction.
• The datasets and platforms you use: RIPE Atlas, CAIDA data, scan datasets, DNS platforms, top lists, telescopes — cite the paper that introduced the instrument, and note its known biases.
• The methodology you extend or question: if you improve or audit a measurement technique, cite its origin and state what you change.
• Adjacent findings that frame significance: related measurements that make your question matter.

Delta-first positioning

Lead each comparison with the difference, not a summary:

• "Prior work measured X from a single vantage point in 2019; we measure it from N vantage points across M regions over a 12-week window in 2026, revealing a diurnal pattern invisible to a snapshot."
• Make the delta a measurement delta: more/better vantage points, longer window, better ground truth, a corrected input, or a newly reachable dataset — not merely "we also study this."

Position against prior datasets and vantage points

This is IMC-specific and easy to underdo:

• If you reuse a dataset, say which version/vintage and why it still supports your claim.
• If you build a new dataset, contrast its coverage and provenance with existing ones — this is also the seed of a Community Contribution Award case (imc-artifact-evaluation).
• Acknowledge the biases of instruments you rely on (e.g., top-list instability, telescope coverage gaps); a reviewer who introduced that instrument may be reading you.

Get the venue attribution right

Measurement work is spread across venues, and misattribution signals unfamiliarity:

• Much scanning/tooling landed at USENIX Security or CCS (e.g., ZMap, Censys), not IMC.
• Systems and protocol design with a measurement flavor is often SIGCOMM, NSDI, or CoNEXT.
• Focused active-measurement work often appears at PAM.
• Check dblp before writing "as shown at IMC" — cite the actual venue (see resources/exemplars/library.md for the sibling-confusion guard).

Keep self-citations double-blind

IMC is double-blind, so positioning against your own prior work is a leak risk:

• Refer to your prior work in the third person: "Prior work [12] measured..." not "In our earlier work we...".
• Do not let a chain of self-citations, a reused private vantage point, or a distinctive testbed name deanonymize you.
• Cite your own datasets by their anonymized/public identifier, not an account or lab URL.

Failure patterns

Pattern	Why it hurts	Fix
Prior work summarized, delta implied	Reviewer cannot see the contribution	Lead every comparison with the measurement delta
Instrument reused without citing its origin/bias	Reads as unaware of the field	Cite the dataset/tool paper and its known biases
Measurement misattributed to IMC	Signals unfamiliarity	Verify the venue on dblp; cite correctly
Self-citation in first person	Double-blind leak	Third-person; anonymized dataset references

Output format

[Lane coverage] phenomenon / datasets-platforms / methodology / adjacent framing — all covered?
[Delta] stated as a measurement delta (vantage points/window/ground truth/dataset)? yes/no
[Dataset positioning] instruments cited with vintage + biases? yes/no
[Venue attribution] measurement prior art cited to the correct venue? yes/no
[Anonymity] self-citations third-person, no infrastructure leak? passed/issues
[Edits] <ordered list>