literature-review-complex-networks

name

literature-review-complex-networks

description

Rigorous literature review workflow for complex networks, nonlinear dynamics, mathematical epidemiology, multi-agent systems, and adjacent physics, mathematics, and data science research. Use when Codex needs to perform early-stage scoping, mid-stage argument refinement, or late-stage paper-writing literature review with verified bibliographic metadata, DOI/author/title/journal/URL cross-checking, citation and journal impact constraints, milestone contributions, hot topics, open problems, evidence grading, and explicit uncertainty marking.

Literature Review Complex Networks

Core Rule

Never invent bibliographic facts, claims, citation counts, impact factors, conclusions, or field narratives. If a source, metric, or claim cannot be verified from reliable evidence, mark it explicitly instead of filling the gap.

Use current web/database lookup for recent literature, citation counts, journal metrics, author affiliations, publication metadata, and any "latest", "hot topic", or "current open problem" request. Prefer primary publisher pages, Crossref, DOI.org, PubMed/Europe PMC, arXiv, Semantic Scholar, OpenAlex, Web of Science/Scopus/Google Scholar if accessible, journal websites, and official Journal Citation Reports or journal metric pages when available. Prioritize searches in Nature, Science, their sub-journals, APS interdisciplinary physics journals, IEEE venues, PNAS, and SAIM/SIAM venues before broadening to other qualified sources.

Workflow

1. Clarify the review stage: early scoping, mid-stage argumentation, or late-stage writing. If the user does not specify, default to early scoping and state that assumption.
2. Define the search space: topic terms, adjacent disciplines, mathematical objects, empirical systems, modeling assumptions, time range, and exclusion criteria.
3. Build a query map across at least three angles when relevant: theory/mechanism, methods/data, and application domain.
4. Search iteratively, starting with the priority venue/publisher list in references/search-and-validation.md, then broaden as needed, and log candidate papers with source provenance.
5. Apply the inclusion rules in references/search-and-validation.md.
6. Read every included paper's abstract. When full text is accessible, also inspect conclusion/discussion sections before making substantive claims.
7. Cross-validate DOI, authors, title, journal/conference, volume/issue/pages or article number, year, URL, and citation count/metric source.
8. Organize results by contribution type: earliest foundational contribution, most important contribution/discovery, latest important progress, active hot topics, unresolved key problems.
9. Mark uncertainty using the labels in references/evidence-and-uncertainty.md.
10. Produce a stage-appropriate output using references/stage-outputs.md.

Domain Coverage

Bias coverage toward physics, applied mathematics, nonlinear science, network science, data science, and mechanistic modeling. For interdisciplinary topics, include connections among:

• complex networks and graph dynamics
• nonlinear dynamics, bifurcation, synchronization, chaos, tipping, control
• mathematical epidemiology, metapopulation models, temporal networks, adaptive behavior, inference
• multi-agent systems, consensus, swarming, game dynamics, learning, distributed control
• data-driven modeling, network reconstruction, causal inference, graph machine learning, simulation and uncertainty quantification

Do not force all domains into every review. Include adjacent domains only when they materially improve the user's question.

Required Review Axes

For most literature reviews, explicitly cover:

• earliest known or earliest verified contribution
• canonical/high-impact contributions and why they matter
• major discoveries, mechanisms, models, methods, or empirical findings
• latest important progress, with exact publication dates when relevant
• currently hot topics and why they are active
• unresolved key problems, including whether they are theoretical, computational, empirical, methodological, or translational
• disagreements, failed assumptions, or limitations where the literature is not settled

Quality Gate

Before finalizing, check that:

• every factual paper claim is attached to a cited source
• every included source satisfies or explicitly explains the citation/impact-factor exception rule
• every source has DOI/URL status noted
• abstracts were read for included papers
• discussion/conclusion sections were checked when full text was available and the claim depends on them
• unverified items use uncertainty labels, not confident prose
• "latest" and "hot topics" are based on current search, not memory

Output Discipline

Use tables for evidence inventories and concise prose for synthesis. Keep bibliographic records structured enough to audit:

Authors | Year | Title | Venue | DOI | URL | Citations | Journal metric | Inclusion rule | Evidence checked | Notes

Separate verified findings from interpretation. When synthesizing, write in a way that preserves provenance: say which paper supports which claim, and distinguish a paper's own conclusion from a broader inference across papers.

References

• Use references/search-and-validation.md for source inclusion rules, database strategy, and metadata cross-checking.
• Use references/stage-outputs.md for early, mid, and late stage review depth and deliverable formats.
• Use references/evidence-and-uncertainty.md for uncertainty labels, confidence grading, and anti-hallucination rules.