Field graph

field-graph

Turn "who actually matters in field X" from a guess into a sourced, ranked graph — for any field, on whatever platform that field actually congregates on.

Method adapted from SimmerChan, 用 Agent 自动扒了一遍知乎推荐系统领域的大V (zhuanlan.zhihu.com/p/2052179994911171124) — which mapped Zhihu's rec-sys field. This skill generalizes it: the graph algorithm is one thing, the platform it runs on is a swappable provider.

Core idea

Two signals, and the second is the important one:

• in-degree (follower / citation count) = fame.
• cross-source count = how many independent seed accounts endorse a person = peer recognition inside the niche.

PageRank intuition: being endorsed by several important, mutually-independent nodes beats being endorsed by many random nodes. The highest-follower account is often a field-adjacent celebrity, not the center. (In the source article: a 14万-follower name was not the center; a 2万-follower node endorsed by 7 independent core people was.)

And forward beats reverse: who an expert chooses to follow / cite is high-signal (30-40% on-topic); their followers are mostly noise (3-10%).

The pipeline — 5 stages

Run these in order. Stages 1–2 are reasoning; 3–5 are the scripts.

1. Define the field (定域)

State the field boundary in one sentence, and the disqualifier ("… but NOT general-purpose ML celebrities"). This is the yardstick for the Stage-4 domain filter. Without it, ranking drifts to whoever everyone follows.

2. Route the field to its platform (找阵地) → pick a provider

Where do this field's people actually congregate and leave a public endorsement graph? This decides the provider. See PLATFORMS.md for the full routing table; the short version:

Field looks like…	Platform / graph	provider
open-source / dev tooling / infra	GitHub follows	github
academic / research / a paper's field	OpenAlex co-authorship	openalex
Chinese tech / 内容创作 / 知乎大V	Zhihu follows	cmd: or --edges (web-access)
influencer / 种草 / creator	X, 小红书, 微博, B站	--edges (web-access)
founders / VC / business	LinkedIn, X	--edges (web-access)

Rule of thumb: use github/openalex when the field lives there (open API); otherwise collect the graph with the web-access skill and rank via --edges.

3. Seed with the field's top ~50 (取种子)

Don't hand-pick 4 accounts and hope. Get the field's head first:

# GitHub: a topic → owners + top contributors of its top repos
scripts/seed_bootstrap.sh --provider github --topic osint --n 40 --contributors > seeds.txt

# OpenAlex: a field → its most-cited authors
scripts/seed_bootstrap.sh --provider openalex --field "natural language processing" --n 50 --mailto [email protected] > seeds.txt

For API-less platforms, get the field's 榜单 / top list via the web-access skill (a "top rec-sys 大V" list, an awards page, a leaderboard) and save as seeds. Eyeball the list — bad seeds poison the whole graph.

4. Expand + rank (扩散 + 交叉信源排序)

Forward BFS over the seeds' endorsement lists, then rank by cross-source count.

# GitHub, 3 BFS rounds, enrich the head
scripts/field_graph.sh @seeds.txt --rounds 3 --promote 12 --top 40 --enrich --csv out.csv

# OpenAlex (co-authorship graph)
scripts/field_graph.sh @seeds.txt --provider openalex --mailto [email protected] --top 30 --enrich

# any platform you can script one hop of:
scripts/field_graph.sh @seeds.txt --provider 'cmd:<command that prints node {}\047s neighbors>'

# platform with no API: rank a graph you harvested via web-access
scripts/field_graph.sh field --edges harvested_follows.tsv --top 40

Then domain-filter (the one manual step): as rounds grow, raw cross-source drifts toward field-adjacent celebrities everyone follows. Keep only nodes whose own work is in the field (Stage-1 boundary). The center = the highest-cross-source node still on-topic after this filter — often a mid-follower specialist.

5. Cross-verify, routed by persona (交叉验证)

A high-ranked node is a handle, not yet a person. Confirm who they are via the source that authoritatively covers their kind — not "check every site":

scripts/cross_verify.sh --gh sokra                 # auto-detect persona from GitHub
scripts/cross_verify.sh --name "Tri Dao" --persona research

code → GitHub/npm/PyPI · research → arXiv/OpenAlex/ORCID · influencer → 小红书/微博/ B站/X (web-access) · business → LinkedIn/Crunchbase/gsxt.gov.cn. Full routing + the self-signed-only rule: SOURCES.md.

Providers (Stage 4 engine)

field_graph.sh is platform-agnostic; a provider answers one question — given a node, which nodes does it endorse?

provider	edge =	needs	node id
github (default)	who a user follows	gh (authed)	login
openalex	a researcher's co-authors	curl, jq	author id A… (or a name, auto-resolved — verify the echo)
cmd:TPL	stdout of TPL with {} = node	whatever TPL uses	anything
--edges FILE	pre-collected seed<TAB>neighbor TSV	—	anything

Adding a field = adding a provider. If you can script "given X, list who X endorses" for a platform, cmd: plugs it in with zero engine changes. If the platform needs a logged-in browser, harvest with web-access → rank via --edges.

Pure shell — no Python, no runtime. github needs gh authenticated (gh auth status); openalex needs curl + jq. Public REST endpoints only.

Reading the output

• Resolution scales with seeds. ~12 seeds tops out ~3 cross-sources; ~45 seeds reaches ~10. More rounds = finer head, but more drift (Stage 4).
• The center is the highest-cross-source node still on-topic after the domain filter — frequently a mid-follower specialist, not the 10k+/14万 name.

Scope & Red Lines

Maps publicly-active contributors of a field from data they chose to make public (their profile, their public follow/citation graph, their signed work). That is the legitimate use, and the only one.

Do not repurpose this to:

• build an exhaustive dossier on one private individual, or de-anonymize someone who deliberately uses a pseudonym / left their profile blank — stop at deliberate anonymity (in the source article: a core node with a blank GitHub was left un-identified, on purpose);
• pivot from a private identifier (phone, personal email, ID) to a person — this tool takes public accounts as seeds, by design, never the reverse;
• attach private/sensitive attributes (psychoprofiles, location inference, protected characteristics) to people.

Hiring / due-diligence: restrict to job-relevant, self-published professional signals and follow local consent law (PIPL / GDPR / FCRA). Real names inferred from an ID are "unconfirmed" until the person signed them publicly. Aggregating "everything about a person" is out of scope on purpose.