Silent Facts — Graph Analytics

We map every tracked company, person, fund, and auditor into one connected network of relationships and analyze it each day. This surfaces structural patterns you can't see in any single filing — who the bridges are, which companies cluster together, and who quietly sits at the center of the web.

Who sits at the center of the web — influence, connectivity, bridges, clusters, and look-alikes. How the system is wired — cross-company directors and auditor concentration / switches. Where strategic money flows — corporate-to-corporate stakes (not 13F). What's coming — likely M&A pairs, implicit competitors, and ripple-effect exposure. What just happened — new relationships added in the last 30 days.

> Top Influencers Influence rank

Influence compounds: you rank higher when other well-connected companies and people connect to you — not just by raw connection count. Higher scores = more central to the whole network.

#	Node	Type	Score
{{ r.rank }}	{{ r.label }}	{{ nodeType(r) }}	{{ r.value }}

> Most Connected Companies Degree

Raw edge count — the hubs of the network.

#	Ticker	Edges
{{ r.rank }}	{{ r.label }}	{{ r.value }}

> Most Connected People Board Reach

Directors and officers with the most cross-company connections.

#	Name	Edges
{{ r.rank }}	{{ r.label }}	{{ r.value }}

> Key Bridges Betweenness Centrality

Nodes that sit on the shortest path between many others — these are gatekeepers that glue clusters together. Removing one would fragment the network.

#	Node	Type	Betweenness
{{ r.rank }}	{{ r.label }}	{{ nodeType(r) }}	{{ r.value }}

> Hidden Communities Connected clusters

We group the network into tightly-connected clusters — revealing spheres of influence, interlocking director groups, and industry cliques that aren't labeled anywhere in the filings.

#{{ c.rank }} · {{ c.label }} {{ c.value }} members

Sector {{ ind.name }} {{ ind.pct }}%

Bound by {{ et.type.replace(/_/g, ' ') }} {{ et.pct }}%

Shared {{ i.type.charAt(0) }}{{ i.name }} {{ i.pct }}%

> Structurally Similar Companies Shared-connection overlap

Companies whose neighborhoods overlap — same auditors, shared board members, common investors, similar supply chains. A competitor map derived from structure, not from industry labels.

#	Pair	Full Names	Overlap
{{ r.rank }}	{{ r.label }}	{{ pairNames(r) }}	{{ r.value }}

> Board Network Power Rankings Cross-Company Directors

Directors who sit on multiple company boards — the hidden influence brokers. A director scoring 3+ is carrying information, norms, and decisions between boardrooms that wouldn't otherwise share a channel.

#	Director	Boards	Companies
{{ r.rank }}	{{ r.label }}	{{ r.value }}	{{ c.ticker \|\| c.name }}

No cross-board directors detected yet.

> Auditor Concentration Blast-Radius View

Market share of each audit firm across the universe. A concentrated market means systemic risk: if any top auditor faces a scandal, restatement wave, or regulatory action, the ripple hits every company in that column.

#	Auditor	Clients	Share	Sample Tickers
{{ r.rank }}	{{ r.label }}	{{ r.value }}	{{ auditorPct(r) }}%	{{ t }}

Share based on {{ auditorTotal }} audited companies in the graph.

> Recent Auditor Switches Last 6 Months

Companies in the universe whose auditor-of-record changed since the last daily snapshot. Every switch is a governance signal — forced rotation, disagreements on accounting, or an upcoming restatement often show up here first.

Detected	Ticker	Company	From	→	To
{{ r.value }}	{{ r.label }}	{{ auditorChangeMeta(r).company_name \|\| '--' }}	{{ auditorChangeMeta(r).from_auditor }}	→	{{ auditorChangeMeta(r).to_auditor }}

No auditor switches detected in the last 6 months. Tracking builds history on each analytics run — initial snapshot acts as baseline.

> Strategic Investments Corporate Stakes (Not 13F)

Company-to-company INVESTED_IN edges — e.g., GOOGL → DXCM, AMZN → MRVL, BRK-B's famous portfolio. Passive index-fund holdings (Vanguard / BlackRock / State Street) are deliberately excluded; this view is about strategic optionality — who is buying into whom outside the public narrative.

Investor	→	Target	Type	Disclosed
{{ r.investor_ticker }}{{ r.investor_name }}	→	{{ r.target_ticker }}{{ r.target_name }}	{{ r.type \|\| 'n/a' }}	{{ r.load_date \|\| '—' }}

Top Strategic Investors

#	Investor	Portfolio	Targets
{{ r.rank }}	{{ r.label }}{{ stakeMeta(r).name }}	{{ r.value }}	{{ t }} +{{ Number(r.value) - (stakeMeta(r).targets \|\| []).length }} more

Top Strategic Investees

#	Target	Backers	Investors
{{ r.rank }}	{{ r.label }}{{ stakeMeta(r).name }}	{{ r.value }}	{{ t }} +{{ Number(r.value) - (stakeMeta(r).investors \|\| []).length }} more

No strategic investments detected yet.

> Smart-Money Convergence where independent signals agree

One board, three independent signals — federal-award surges, insider buy clusters, and smart-money 13F accumulation. Toggle the chips: pick one for that signal's standalone leaderboard, any two for the pair intersection, all three for the rare full convergence. Selecting more chips ANDs them together — each name shown fires every selected signal — and re-ranks by the selected signals' combined strength. Public-data convergence (USAspending awards + SEC Form 4 + 13F holdings). Research signal — not investment advice.

| {{ convFiltered.length }} name{{ convFiltered.length === 1 ? '' : 's' }} · {{ convStateLabel }}

Select at least one signal chip above.

No names fire {{ convStateLabel }} right now — a sparse/empty intersection means the convergence is rare, not broken.

#	Ticker	Combined	Gov	Insider	{{ convTopFunds ? 'Whale*' : 'Whale' }}	Signals
{{ i + 1 }}	{{ r.label }}	{{ r._score.toFixed(2) }}	{{ (r._m.gov_score\|\|0).toFixed(2) }}	{{ (r._m.insider_score\|\|0).toFixed(2) }}	{{ ((convTopFunds ? r._m.whale_top_score : r._m.whale_score)\|\|0).toFixed(2) }}	{{ (r._m.which_fired\|\|[]).join(' · ') }}

> Federal-Funding Intensity TTM federal obligations ÷ company size

Companies most dependent on federal spending, ranked by trailing-12-month USAspending obligations relative to size — gov$ ÷ revenue (primary), with gov$ ÷ market cap where priced. A high ratio flags a name whose government revenue is material to the business — a forward-revenue lever the market can underprice, especially for small caps. Federal obligations (USAspending) vs as-reported financials (SEC XBRL). Research signal — not investment advice.

No gov-funding data yet — the federal-award backfill + XBRL revenue need to populate.

#	Ticker	Gov / Rev	Gov $ (TTM)	Revenue	Gov / Mkt Cap	Top agency
{{ r.rank }}	{{ r.label }}	{{ gfMeta(r).gov_to_revenue_pct }}%	${{ (gfMeta(r).gov_ttm_usd/1e6).toLocaleString(undefined,{maximumFractionDigits:0}) }}M {{ gfMeta(r).gov_basis }}	${{ (gfMeta(r).revenue_usd/1e6).toLocaleString(undefined,{maximumFractionDigits:0}) }}M	{{ gfMeta(r).gov_to_mktcap_pct != null ? gfMeta(r).gov_to_mktcap_pct + '%' : '—' }}	{{ gfMeta(r).top_agency }}

> Insider Buy Clusters Multiple insiders buying on the open market

Issuers where multiple distinct insiders made open-market purchases (Form 4 code P, excluding scheduled 10b5-1 plans) in the trailing window. Insiders sell for many reasons but buy for one — a buy cluster is the highest-conviction discretionary signal in the filings. Score rewards breadth (distinct buyers), seniority, and net dollars. Open-market insider purchases (SEC Form 4). Research signal — not investment advice.

No active insider buy clusters in the trailing window.

#	Ticker	Score	Insiders	Buys	Net $	Latest
{{ r.rank }}	{{ r.label }}	{{ r.value }}	{{ icMeta(r).n_insiders }}	{{ icMeta(r).n_buys }}	${{ (icMeta(r).net_buy_usd/1e6).toLocaleString(undefined,{maximumFractionDigits:1}) }}M	{{ icMeta(r).latest_date }}

> Death-Spiral Watch Deterioration sequences from 4yr of 8-K events

Tickers where multiple negative 8-K events stack up over time — the patterns a single quarter can't see: auditor change → restatement → delisting, exec exodus → impairment, covenant default → delisting. Score rewards severity, convergence (distinct event types), recency, and canonical deterioration ordering. Public-filing event correlation. Research signal — not investment advice.

No active deterioration sequences — no tracked name currently shows ≥2 stacked negative 8-K events in the trailing window.

#	Ticker	Score	Sequence	Events	Span
{{ r.rank }}	{{ r.label }}	{{ r.value }}	{{ dsMeta(r).sequence_label }}	{{ dsMeta(r).n_events }}	{{ Math.round((dsMeta(r).span_days\|\|0)/30) }} mo

> M&A Prediction Signals Unconnected Pairs, High Signal Overlap

Pairs of companies that are not directly connected in the graph but share an unusual amount of governance and supply-chain infrastructure. Score = 2×shared directors + shared auditor + shared suppliers + 0.5×same industry + 0.5×max(acquirer activity). The activity term boosts pairs where one side has a recent M&A track record (Active Acquirers) — JNJ at 6 deals/24mo, MICROSOFT at 3, etc. — reflecting that a known buyer is statistically more likely to announce a *next* deal than two passive peers with the same shared signals. These are candidates where an announced deal would look "obvious in hindsight."

#	Pair	Score	Board	Auditor	Suppliers	Industry	Activity
{{ r.rank }}	{{ maMeta(r).a_ticker }} ↔ {{ maMeta(r).b_ticker }} {{ maMeta(r).a_name }} & {{ maMeta(r).b_name }}	{{ r.value }}	{{ maMeta(r).shared_board }}	{{ maMeta(r).shared_auditor }}	{{ maMeta(r).shared_suppliers }}	{{ maMeta(r).same_industry }}	{{ Math.max(maMeta(r).a_activity \|\| 0, maMeta(r).b_activity \|\| 0) }}

No high-signal unconnected pairs surfaced (need ≥2.0 score).

> Acquirer Playbook Per-acquirer M&A pattern fingerprint

What KIND of deals each serial acquirer does — extracted from their actual deal history. Five dimensions: typical sectors, deal-size range, cadence (deals/year + dry-spell weeks), structure (cash/stock/mixed), hostile-percentage. Plus an LLM-generated 2-3 sentence narrative. Reads as "JNJ buys medtech at $14.5B median in all-cash deals, ~4 per year" — the actionable summary that lets you predict not just *whether* JNJ buys next, but *what kind*.

{{ playbookMeta(p).deal_count }} recent deals

Sectors {{ s.name }} {{ s.pct }}%

Deal Size Median ${{ playbookMeta(p).deal_size.median_b }}B Range ${{ playbookMeta(p).deal_size.min_b }}B–${{ playbookMeta(p).deal_size.max_b }}B ({{ playbookMeta(p).deal_size.n_disclosed }}/{{ playbookMeta(p).deal_count }} disclosed)

Cadence {{ playbookMeta(p).cadence.per_year }}/year Last {{ playbookMeta(p).cadence.weeks_since_last }}w ago Max gap {{ playbookMeta(p).cadence.longest_dry_spell_weeks }}w

Structure {{ s.name }} {{ s.pct }}%

Hostile {{ playbookMeta(p).hostile_pct }}%

> Likely Next Deal Active Acquirers × Prediction Signals join

For each serial acquirer (top of the Active Acquirers leaderboard), the highest-scoring unconnected pair candidates from the M&A Prediction Signals table — combining the *propensity* signal (this company has been buying) with the *latent connection* signal (this target is structurally close). Each row reads as a real watchlist candidate: "JNJ has 6 recent deals; the strongest unconnected target is AAPL (board interlock via Alex Gorsky)."

#	Acquirer	Recent Deals	Likely Next Targets
{{ idx + 1 }}	{{ likelyMeta(r).acquirer_ticker }} {{ likelyMeta(r).acquirer_name }}	{{ likelyMeta(r).acquirer_deal_count }}	{{ c.ticker }} ${{ c.target_mc_b }}B size? {{ c.score }} [{{ c.strongest }}]

> Active Acquirers Company-side M&A — historical buying velocity (last 24mo)

Companies that have announced or closed an acquisition in the last 24 months — a track record signals who's most likely to buy next, which the pairwise table above can't capture on its own. Drawn from tender-offer filings, deal press releases, and the acquisitions disclosed in quarterly and annual reports — so it captures both large-cap serial buyers (e.g. J&J — Intra-Cellular, Shockwave) and active mid-cap acquirers (Masimo, Wiz, Preqin).

#	Acquirer	Deals (24mo)	Recent Targets
{{ r.rank }}	{{ acquirerMeta(r).acquirer_ticker }} — {{ acquirerMeta(r).acquirer_name }}	{{ r.value }}	{{ t.ticker }} {{ (t.name \|\| '?').substring(0, 28) }} ⚔

No acquisition activity in the last 24 months in the current data.

> Competitive Intelligence Implicit Competitors — Same Industry, Shared Supply Chain, No Direct Link

Company pairs in the same industry that share 3+ suppliers or customers but have no direct relationship in the graph. Think CRM ↔ ORCL or INTC ↔ ON — obvious competitors whose rivalry is invisible to simple ticker searches. Use this to stress-test sector assumptions and find pair-trade setups.

#	Pair	Industry	Shared	Customers	Suppliers
{{ r.rank }}	{{ compMeta(r).a_ticker }} ↔ {{ compMeta(r).b_ticker }} {{ compMeta(r).a_name }} vs {{ compMeta(r).b_name }}	{{ compMeta(r).industry }}	{{ r.value }}	{{ compMeta(r).shared_customers }}	{{ compMeta(r).shared_suppliers }}

No qualifying competitor pairs surfaced (need ≥3 shared supply-chain links).

> Risk Contagion Blast Radius — If This Ticker Breaks, Who's Exposed?

For every ticker, count the distinct other tickers reachable via a distress-transmitting channel: a shared director (board, weight 2.0), the same audit firm (auditor, weight 1.5), a direct supply-chain link (supply, weight 1.0), or a strategic stake (investment, weight 0.5). Higher blast_score = more peers likely to feel the shock. Shared-auditor counts look uniform because ~105 tickers use EY/Deloitte/PwC — that is the concentration risk. The Lookup panel below runs the same query on-demand for any ticker and lists the exposed names by channel.

Blast-Radius Lookup

Seed: {{ contagionResult.ticker }} {{ contagionResult.name }} blast_score {{ contagionResult.blast_score }}

{{ channel }} {{ list.length }} exposed

No exposure via this channel.

{{ x.ticker }} {{ x.name }} via {{ x.via }}

Top Blast-Radius Hubs

#	Ticker	Blast Score	Board	Auditor	Supply	Investment
{{ r.rank }}	{{ r.label }} {{ contagionMeta(r).name }}	{{ r.value }}	{{ contagionMeta(r).board_n }}	{{ contagionMeta(r).auditor_n }}	{{ contagionMeta(r).supply_n }}	{{ contagionMeta(r).invest_n }}

Contagion hubs not computed yet — run --contagion.

> Recent Relationship Changes Last 30 Days

New relationships added to the graph in the last 30 days, drawn from filings ingested during the daily refresh. Think of this as a changelog for the corporate network — new board seats, fresh supplier links, recently-disclosed investors.

{{ filteredRecent.length }} of {{ recentChanges.length }} events

Date	Source	Relationship	Target	Context
{{ r.value }}	{{ recentMeta(r).source }} {{ (recentMeta(r).src_label \|\| '?').charAt(0) }}	{{ recentMeta(r).rel_type }}	{{ recentMeta(r).target }} {{ (recentMeta(r).tgt_label \|\| '?').charAt(0) }}	{{ recentMeta(r).context \|\| '(no context)' }} {{ recentMeta(r).context \|\| '(no context)' }}

No relationship changes detected in the last 30 days. Run the daily pipeline to refresh.

How to read this page

What it covers: every company, person, fund, and auditor we track, connected by the relationships pulled from their SEC filings — board seats, auditors, suppliers, partners, investors, lenders, competitors, industry, and location.

What it does: we map those connections into one network and measure it the way you'd analyze any web of relationships — who sits at the center of influence, who acts as a bridge between otherwise-separate groups, and which companies cluster together. These structural patterns aren't visible in any single filing.

Freshness: recomputed every night from the latest filings, so the rankings reflect the current state of the network.

Change tracking: we compare each night's snapshot to the previous one to surface what's new — recently formed relationships and auditor switches (shown for the last 6 months, building up over time).

All figures are derived from public SEC filings. Research signal — not investment advice.