Quantitative Mapping of Corporate Control: Affiliation vs. Ownership Networks
The complexity of modern financial markets is often obscured within the sheer volume of regulatory disclosures. By applying automated knowledge graph extraction to a random sample of 2,000 SEC Form 4 filings, a researcher can move beyond individual transactions to visualize the underlying architecture of the market. This analysis utilizes bipartite network mapping to distinguish between two fundamental types of market connectivity: professional affiliations and direct legal ownership. By quantifying metrics such as density and the Power Law coefficient ($\alpha$), the study analyzes how information and authority flow through seemingly disparate entities. While these filings are legally mandated for insiders and 10% owners, the network analysis reveals that market activity is not distributed evenly among them. Instead, the study identifies how a small group of "High-Frequency Reporters" drives the majority of transactional visibility across disparate corporate entities.
Automated Knowledge Graph Extraction of 2,000 random SEC Form 4 filings reveals a sparse, decentralized Affiliation Network. With a Network Density of 0.00533, the data indicates that only a minute fraction of potential professional connections among the 57 identified nodes are realized, reflecting a fragmented landscape of institutional associations. The Degree Distribution follows a power-law trend with a coefficient ($\alpha$) of 2.03, indicating that most entities maintain a single connection while rare hubs reach a Max Hub Degree of 3. This profile suggests that while the broader market is loosely connected, specific actors serve as critical bridges for professional influence across organizations. In other words, it suggests act as institutional bridges; these entities are associated with more frequent transacting across different boards, suggesting that their professional influence is mirrored by a higher volume of regulatory filings across the entities they are affiliated with.
The network topology distinguishes between independent clusters and interconnected institutional webs via divergent color mapping. An Average Connectivity of 0.60 highlights that most nodes operate in isolation or small pairs rather than dense cliques. By mapping these professional bonds, the analysis exposes the "social web" of the market, identifying sparse channels through which strategic information and oversight flow between separate entities.
The extraction simultaneously identifies a highly centralized Ownership Network. The system processed 217 total nodes connected by 34 direct ownership edges, resulting in an extremely low Network Density of 0.00073. The Power Law coefficient ($\alpha$) of 2.04 mathematically confirms a "heavy-tail" distribution where power is concentrated. The network is anchored by dominant "Super-Owners" with a Max Hub Degree of 7, representing entities or individuals who exert direct legal control over diverse corporate portfolios simultaneously.
The graph portrays a hub-and-spoke model, where central figures project authority across a wide array of target entities. With an Average Connectivity of 0.31, most participants remain unconnected to larger clusters, highlighting the exclusivity of multi-entity control. The data shows that a handful of insiders—often including family-linked nodes like "wife" or "son"—are responsible for a disproportionate share of the total reporting volume. This reveals that "ownership" in the SEC context is a high-velocity activity, where a tiny minority of nodes generates the vast majority of the market's transactional signals.
In conclusion, the structural contrast between these two networks highlights the different modalities of market participation. While both networks exhibit power-law characteristics with similar coefficients ($\alpha \approx 2.0$), the ownership network demonstrates a much higher degree of concentration with a Max Hub Degree of 7 compared to the affiliation network's 3. These findings demonstrate that automated graph analysis can effectively isolate the channels of professional influence from the hubs of legal authority, providing a clearer quantitative baseline for market oversight and regulatory policy.