Skill UI

This guide outlines the rigorous analytical methodology required for Operations Research (OR) manuscripts. It instructs users on selecting the appropriate mathematical machinery—such as duality, dynamic programming, convergence analysis, or concentration inequalities—to establish provable guarantees, approximation factors, and algorithmic complexity. It ensures that claims are backed by rigorous proofs, moving beyond mere heuristics.

This skill guides authors on strategically positioning data mining research for the SIAM International Conference on Data Mining (SDM). It helps adjust manuscript framing, emphasizing mathematical rigor, statistical depth, and algorithmic contribution suitable for top Computer Science venues. Use it for venue selection, understanding reviewer expectations, and structuring evidence to meet the high standards of SIAM-style research.

A comprehensive guide for researchers developing systems or algorithms, detailing the necessary level of evidence required for performance claims (e.g., bounds, real-world performance, learning guarantees). It emphasizes scientific rigor, statistical reporting (confidence intervals), assumption testing, and data provenance to ensure findings are robust and reproducible in academic settings.

A comprehensive guide for designing and auditing visualization experiments for high-stakes academic submissions (e.g., IEEE VIS). It details how to match evidence—such as controlled studies, power analyses, and benchmarks—to the specific type of claim (perceptual, algorithmic, system). Learn best practices for statistical rigor, CVD-safe encoding, and maintaining full provenance to ensure your findings are scientifically sound and trustworthy.

NetworkX is a powerful Python package designed for creating, manipulating, and analyzing complex networks and graphs. It supports various graph types (Directed, Undirected, MultiGraphs) and provides extensive functionality for graph algorithms, including shortest path calculation, centrality measures (like PageRank), community detection, and connectivity checks. Users can generate synthetic networks, read/write data from multiple formats (GraphML, CSV, Pandas), and visualize results using Matplotlib. Ideal for social network analysis, biological research, and system modeling.

JurisRank is a computational tool that analyzes citation networks of Argentine Supreme Court (CSJN) jurisprudence using a peer-reviewed PageRank algorithm with temporal decay. It measures the jurisprudential authority of legal precedents, allowing users to rank competing cases by influence, trace the evolution of legal doctrine, and detect potential constitutional drift. Ideal for intensive legal research, due diligence, and academic analysis of legal trends in Argentina.