IMC Related Work & Positioning
Guides the framing, dataset citation, and literature positioning for ACM Internet Measurement Conference (IMC) academic papers.
The IMC Related Work skill helps academic researchers and computer scientists draft, audit, and refine the related work sections of ACM Internet Measurement Conference (IMC) submissions. It ensures measurement-specific framing by emphasizing delta-first comparisons (vantage points, measurement windows, and ground truth), proper attribution of foundational platforms and datasets (such as RIPE Atlas and CAIDA), accurate venue differentiation between IMC and related venues like SIGCOMM, PAM, or USENIX Security, and strict adherence to double-blind self-citation requirements.
主要功能
01Delta-first positioning focusing on vantage points, collection windows, and ground truth
02Double-blind self-citation audit to prevent de-anonymization via testbeds or lab references
031,079 GitHub stars
04Proper citation guidelines for measurement platforms, datasets, tools, and their known biases
05Accurate venue disambiguation across IMC, PAM, SIGCOMM, NSDI, and USENIX Security
06Structured review output format to systematically evaluate related-work lane coverage
使用场景
01Highlighting scientific novelties and dataset contributions against prior internet measurement literature
02Drafting the related work and background section for an ACM IMC paper submission
03Auditing an existing networking measurement manuscript for double-blind anonymity compliance