TLDR
The ownership graph built by the institutional forensics script contains exactly 33 static edges (fixed connections) representing verified corporate relationships — officer appointments, registered agent designations, and parent-subsidiary links — plus dynamic wire flow edges from 224 parsed transfers totaling $24.1 million. Together they enable pass-through detection, identifying entities that function as financial conduits rather than genuine economic actors (PAPER TRAIL Project, 2026).
Two Kinds of Edges
A graph (a map of connections between entities) is only as useful as its edges (the lines connecting them). In the Epstein ownership graph, edges come from two fundamentally different sources, and understanding the distinction is critical.
The 33 static edges are verified corporate relationships. These are officer appointments confirmed through state corporate registries (New York Department of State, Delaware Division of Corporations, USVI corporate records), registered agent designations, and parent-subsidiary links documented in court filings and regulatory orders. They do not change over time. Jeffrey Epstein serving as officer of NES LLC is a static fact recorded in a state database (PAPER TRAIL Project, 2026).
The dynamic edges come from the 224 parsed wire transfers totaling $24.1 million. Each wire creates a temporal edge — a connection between two entities that existed at a specific date, carrying a specific amount, with a specific memo. Southern Financial LLC wiring $7.75 million to Harlequin Dane LLC across seven transactions is not a permanent structural relationship. It is a financial event that occurred during a particular window (PAPER TRAIL Project, 2026).
The ownership graph overlays both types. Static edges provide the skeleton. Dynamic edges reveal what the skeleton was built to carry.
The Hub Structure
Five individuals connect the static edge network. Epstein appears on 8 entities. Indyke appears on 9 or more. Kahn appears on 5. Visoski appears on 4 aviation entities. Kellerhals appears on 1. Between them, they account for the majority of officer-to-entity edges in the graph (PAPER TRAIL Project, 2026).
The geographic structure is equally concentrated. At least 7 entities share the registered address 6100 Red Hook Quarter, St. Thomas, USVI: Southern Financial LLC, NES LLC, JEGE LLC, F T Real Estate, Bella Klein TTEE, Southern Country International, and Financial Trust Company. This single address is the USVI nexus — the node through which the offshore corporate architecture converges (PAPER TRAIL Project, 2026).
The corporate migration pattern adds a temporal dimension to the static edges. JEGE Inc. was incorporated in Delaware in 2000. JEGE LLC was formed in the USVI in 2012. Plan D LLC followed in the USVI. This is not random corporate activity. It is a systematic relocation from New York and Delaware jurisdictions to the tax-advantaged USVI, mirroring the broader entity migration documented across the ownership graph (PAPER TRAIL Project, 2026).
Pass-Through Detection
The most analytically valuable output of combining static and dynamic edges is pass-through detection. A pass-through entity is one where money enters and exits with minimal retention — a node that functions as a conduit rather than an economic actor performing genuine business (PAPER TRAIL Project, 2026).
Harlequin Dane LLC is the clearest example in the corpus. Seven wires totaling $7.75 million flowed in from Southern Financial LLC. Outbound flows went to Seaford Avenue Capital, Signature Title Group, JR Watersports, Jetsmarter, and — critically — $221,000 in "loan payments" back to Southern Financial's own Deutsche Bank account. The circular return is a layering signature (a money-laundering technique where funds pass through intermediaries to disguise their origin): money leaving an entity and returning to the entity that originated it, having passed through an intermediary (PAPER TRAIL Project, 2026).
Southern Financial LLC itself shows the highest volume in the graph: $606.9 million in inflows against $194.7 million in outflows, a net of $412.3 million. This is the entity the USVI Economic Development Commission granted a 90% income tax exemption for "DNA database consulting" — a business the USVI Attorney General later found had no evidence of legitimate scientific work (PAPER TRAIL Project, 2026).
What the Graph Exports
The ownership graph module exports two files. The edges file contains the edge list with relationship type, source entity, target entity, and jurisdiction. The nodes file contains node attributes including entity type, jurisdiction, formation date, and aggregate financial volume from wire transfers (PAPER TRAIL Project, 2026).
Together with the other five modules in the institutional forensics script, the ownership graph feeds into the accountability matrix that assigns complicity tiers. An entity's position in the graph — how many edges connect to it, whether it sits on pass-through paths, whether it bridges static corporate relationships to dynamic financial flows — determines its structural importance score.
Thirty-three static edges and 224 wire transfers. The skeleton and what it carried. The architecture and the money that moved through it.
References
PAPER TRAIL Project. (2026). Institutional forensics ownership_graph module [Script]. app/scripts/18_institutional_analysis.py.
PAPER TRAIL Project. (2026). Ownership edges export [Data]. _exports/institutional/ownership_edges.csv.
PAPER TRAIL Project. (2026). Ownership nodes export [Data]. _exports/institutional/ownership_nodes.csv.
PAPER TRAIL Project. (2026). Entity ownership research [Data]. research/ENTITY_OWNERSHIP.md.
PAPER TRAIL Project. (2026). Corporate registry verification [Data]. research/CORROBORATION_REPORT.md.
PAPER TRAIL Project. (2026). Wire flow network [Data]. research/td_bank_sar_extraction.md.
PAPER TRAIL Project. (2026). Wire transfers table: 224 rows, $24.1 million [Data]. PostgreSQL db=epstein_files.