Przemysław Szufel | Searcharxiv

arXiv2026

HIF: The hypergraph interchange format for higher-order networks

Many empirical systems contain complex interactions of arbitrary size, representing, for example, chemical reactions, social groups, co-authorship relationships, and ecological dependencies. These interactions are known as higher-order interactions and the collection of these interactions comprise a higher-order network, or hypergraph. Hypergraphs have established themselves as a popular and versatile mathematical representation of such systems and a number of software packages written in various programming languages have been designed to analyze these networks. However, the ecosystem of higher-order network analysis software is fragmented due to specialization of each software's programming interface and compatible data representations. To enable seamless data exchange between higher-order network analysis software packages, we introduce the Hypergraph Interchange Format (HIF), a standardized format for storing higher-order network data. HIF supports multiple types of higher-order networks, including undirected hypergraphs, directed hypergraphs, and abstract simplicial complexes, while actively exploring extensions to represent multiplex hypergraphs, temporal hypergraphs, and ordered hypergraphs. To accommodate the wide variety of metadata used in different contexts, HIF also includes support for attributes associated with nodes, edges, and incidences. This initiative is a collaborative effort involving authors, maintainers, and contributors from prominent hypergraph software packages. This project introduces a JSON schema with corresponding documentation and unit tests, example HIF-compliant datasets, and tutorials demonstrating the use of HIF with several popular higher-order network analysis software packages.

physics.soc-ph↗

arXiv2025

Ukrainian-style oligarchic economies: how concentrated power undermines value added in production chains

Oligarchic control exerts significant distortions on economic efficiency. Ukraine exemplifies this phenomenon, where oligarchs dominate key sectors and achieve economies of scale through vertical integration of coal mines, steel mills, and power plants while controlling critical infrastructure (e.g. access to transportation networks) to stifle competition. Their Soviet-era production chain monopolization strategies, coupled with political patronage networks (including both local and national governments), reinforce systemic inefficiencies and barriers to market entry. Although existing studies highlight the developmental benefits of de-oligarchization, this work advances the literature through computational modeling. We develop an agent-based model of a partially oligarch-controlled economy, where firms with heterogeneous production functions interact within a value-added network. Through numerical simulations, we quantify how different de-oligarchization policies affect aggregate GDP growth. The results indicate that the optimal de-oligarchization strategies are determined by the position of the oligarch in the production chain. Depending on the oligarch's position, dismantling oligarchic structures should either focus on removing oligarchs' access to raw materials or on breaking oligarchs' influence on other transactions in the production chain.

econ.GN↗