Carolina Mattsson (p), Frank Takes, Elke M. Heemskerk, Cees Diks, Gert Buiten, Albert Faber, Peter M.A. Sloot

Laszlo Lorincz

Production networks are integral to economic dynamics, yet dis-aggregated network data on inter-firm trade is rarely collected and often proprietary. Here we situate company-level production networks within a wider space of networks that are different in nature, but similar in local connectivity structure. Through this lens, we study a regional and a national network of inferred trade relationships reconstructed from Dutch national economic statistics and re-interpret prior empirical findings. We find that company-level production networks have so-called functional structure, as previously identified in protein-protein interaction (PPI) networks. Functional networks are distinctive in their over-representation of closed squares, which we quantify using an existing measure called spectral bipartivity. Shared local connectivity structure lets us ferry insights between domains. PPI networks are shaped by complementarity, rather than homophily, and we use multi-layer directed configuration models to show that this principle explains the emergence of functional structure in production networks. Companies are especially similar to their close competitors, not to their trading partners. Our findings have practical implications for the analysis of production networks and give us precise terms for the local structural features that may be key to understanding their routine function, failure, and growth.

This presentation will focus on the implications of functional structure for regional industrial clusters that rely on energy-intensive production processes. Our climate is changing at an alarming rate due to the greenhouse gasses released during economic activity, and policymakers in the Netherlands and elsewhere are tasked with facilitating the transition to a carbon-neutral economy. We highlight several network-structural features of functional networks in the context of industrial clusters, and draw attention to implications of particular concern for policymakers.

Tobias Reisch (p), Georg Heiler, Christian Diem, Stefan Thurner

Carolina Mattsson

National economies rest on networks of millions of customer-supplier relations. Some companies --in the case of their default-- can trigger significant cascades of shock in the supply-chain network and are thus systemically risky. Up to now, systemic risk of individual companies was practically not quantifiable, due to the unavailability of firm-level transaction data. So far, economic shocks are typically studied in the framework of input-output analysis on the industry-level that can't relate risk to individual firms. Exact firm-level supply networks based on tax or payment data exist only for very few countries. Here we explore to what extent telecommunication data can be used as an inexpensive, easily available, and real-time alternative to reconstruct national supply networks on the firm-level. We find that the conditional probability of correctly identifying a true customer-supplier link ––given a communication link exists–– is about 90%. This quality level allows us to reliably estimate a systemic risk profile of an entire country that serves as a proxy for the resilience of its economy. In particular, we are able to identify the high systemic risk companies. We find that 65 firms have the potential to trigger large cascades of disruption in production chains that could cause severe damages in the economy. We verify that the topological features of the inter-firm communication network are highly similar to national production networks with exact firm-level interactions.

Johannes Wachs (p)

Tobias Reisch

Open source software ecosystems consist of thousands of interdependent libraries, which users can combine to great effect. Recent work has pointed out two kinds of risks in these systems: that technical problems like bugs and vulnerabilities can spread through dependency links, and that relatively few developers are responsible for maintaining even the most widely used libraries. However, a more holistic diagnosis of systemic risk in software ecosystem should consider how these social and technical sources of risk interact and amplify one another. Motivated by the observation that the same individuals maintain several libraries within dependency networks, we present a methodological framework to measure risk in software ecosystems as a function of both dependencies and developers. In our models, a library's chance of failure increases as its developers leave and as its upstream dependencies fail. We apply our method to data from the Rust ecosystem, highlighting several systemically important libraries that are overlooked when only considering technical dependencies. We compare potential interventions, seeking better ways to deploy limited developer resources with a view to improving overall ecosystem health and software supply chain resilience.

Laszlo Lorincz (p), Sándor Juhász, Rebeka O. Szabó

Johannes Wachs

The web of relationships between companies is essential for markets, as they convey information, resources and knowledge within a social structure. The embeddedness of firms in these relational structures partly arises from pure business interest and partly from social connections, such as ownership relations or the geographical proximity of companies. However, studying the interplay between such relationships is rare at large-scale, because of their significant data demand.
In this project, we search for the determinants of business tie creation and tie persistence. We combine administrative datasets on firms to uncover the evolution of inter-firm business connections in Hungary between 2016-17. We connect firms through their co-ownership ties, and business transactions. This allows us to show how direct and indirect connections in this multiplex network influence the creation and persistence of business transactions together with geographical proximity. We find that both direct and indirect connections contribute to creation and maintenance of business transactions together with geographical and industry proximities. Further, we observe a diminishing effect, i.e. an additional positive effect of such a connection is decreased if they appear together with another connection.