Adapting to disruptions: Managing supply chain resilience through product rerouting.

Supply chain disruptions may cause shortages of essential goods, affecting millions of individuals. We propose a perspective to address this problem via reroute flexibility. This is the ability to substitute and reroute products along existing pathways, hence without requiring the creation of new connections. To showcase the potential of this approach, we examine the US opioid distribution system. We reconstruct over 40 billion distribution routes and quantify the effectiveness of reroute flexibility in mitigating shortages. We demonstrate that flexibility (i) reduces the severity of shortages and (ii) delays the time until they become critical. Moreover, our findings reveal that while increased flexibility alleviates shortages, it comes at the cost of increased complexity: We demonstrate that reroute flexibility increases alternative path usage and slows down the distribution system. Our method enhances decision-makers' ability to manage the resilience of supply chains.

Struggling With Change: The Fragile Resilience of Collectives.

Collectives form nonequilibrium social structures characterized by volatile dynamics. Individuals join or leave. Social relations change quickly. Therefore, unlike engineered or ecological systems, a resilient reference state cannot be defined. We propose a novel resilience measure combining two dimensions: robustness and adaptivity. We demonstrate how they can be quantified using data from a software-developer collective. Our analysis reveals a resilience life cycle (i.e., stages of increasing resilience are followed by stages of decreasing resilience). We explain the reasons for these observed dynamics and provide a formal model to reproduce them. The resilience life cycle allows distinguishing between short-term resilience, given by a sequence of resilient states, and long-term resilience, which requires collectives to survive through different cycles.

Reconstructing signed relations from interaction data.

Positive and negative relations play an essential role in human behavior and shape the communities we live in. Despite their importance, data about signed relations is rare and commonly gathered through surveys. Interaction data is more abundant, for instance, in the form of proximity or communication data. So far, though, it could not be utilized to detect signed relations. In this paper, we show how the underlying signed relations can be extracted with such data. Employing a statistical network approach, we construct networks of signed relations in five communities. We then show that these relations correspond to the ones reported by the individuals themselves. Additionally, using inferred relations, we study the homophily of individuals with respect to gender, religious beliefs, and financial backgrounds. Finally, we study group cohesion in the analyzed communities by evaluating triad statistics in the reconstructed signed network.

Locating community smells in software development processes using higher-order network centralities.

Community smells are negative patterns in software development teams' interactions that impede their ability to successfully create software. Examples are team members working in isolation, lack of communication and collaboration across departments or sub-teams, or areas of the codebase where only a few team members can work on. Current approaches aim to detect community smells by analysing static network representations of software teams' interaction structures. In doing so, they are insufficient to locate community smells within development processes. Extending beyond the capabilities of traditional social network analysis, we show that higher-order network models provide a robust means of revealing such hidden patterns and complex relationships. To this end, we develop a set of centrality measures based on the MOGen higher-order network model and show their effectiveness in predicting influential nodes using five empirical datasets. We then employ these measures for a comprehensive analysis of a product team at the German IT security company genua GmbH, showcasing our method's success in identifying and locating community smells. Specifically, we uncover critical community smells in two areas of the team's development process. Semi-structured interviews with five team members validate our findings: while the team was aware of one community smell and employed measures to address it, it was not aware of the second. This highlights the potential of our approach as a robust tool for identifying and addressing community smells in software development teams. More generally, our work contributes to the social network analysis field with a powerful set of higher-order network centralities that effectively capture community dynamics and indirect relationships.

Predicting variable-length paths in networked systems using multi-order generative models.

Apart from nodes and links, for many networked systems, we have access to data on paths, i.e., collections of temporally ordered variable-length node sequences that are constrained by the system's topology. Understanding the patterns in such data is key to advancing our understanding of the structure and dynamics of complex systems. Moreover, the ability to accurately model and predict paths is important for engineered systems, e.g., to optimise supply chains or provide smart mobility services. Here, we introduce MOGen, a generative modelling framework that enables both next-element and out-of-sample prediction in paths with high accuracy and consistency. It features a model selection approach that automatically determines the optimal model directly from data, effectively making MOGen parameter-free. Using empirical data, we show that our method outperforms state-of-the-art sequence modelling techniques. We further introduce a mathematical formalism that links higher-order models of paths to transition matrices of random walks in multi-layer networks.

The Downside of Heterogeneity: How Established Relations Counteract Systemic Adaptivity in Tasks Assignments.

We study the lock-in effect in a network of task assignments. Agents have a heterogeneous fitness for solving tasks and can redistribute unfinished tasks to other agents. They learn over time to whom to reassign tasks and preferably choose agents with higher fitness. A lock-in occurs if reassignments can no longer adapt. Agents overwhelmed with tasks then fail, leading to failure cascades. We find that the probability for lock-ins and systemic failures increase with the heterogeneity in fitness values. To study this dependence, we use the Shannon entropy of the network of task assignments. A detailed discussion links our findings to the problem of resilience and observations in social systems.

Configuration models as an urn problem.

A fundamental issue of network data science is the ability to discern observed features that can be expected at random from those beyond such expectations. Configuration models play a crucial role there, allowing us to compare observations against degree-corrected null-models. Nonetheless, existing formulations have limited large-scale data analysis applications either because they require expensive Monte-Carlo simulations or lack the required flexibility to model real-world systems. With the generalized hypergeometric ensemble, we address both problems. To achieve this, we map the configuration model to an urn problem, where edges are represented as balls in an appropriately constructed urn. Doing so, we obtain the generalized hypergeometric ensemble of random graphs: a random graph model reproducing and extending the properties of standard configuration models, with the critical advantage of a closed-form probability distribution.

Improving the Robustness of Online Social Networks: A Simulation Approach of Network Interventions.

Online social networks (OSN) are prime examples of socio-technical systems in which individuals interact via a technical platform. OSN are very volatile because users enter and exit and frequently change their interactions. This makes the robustness of such systems difficult to measure and to control. To quantify robustness, we propose a coreness value obtained from the directed interaction network. We study the emergence of large drop-out cascades of users leaving the OSN by means of an agent-based model. For agents, we define a utility function that depends on their relative reputation and their costs for interactions. The decision of agents to leave the OSN depends on this utility. Our aim is to prevent drop-out cascades by influencing specific agents with low utility. We identify strategies to control agents in the core and the periphery of the OSN such that drop-out cascades are significantly reduced, and the robustness of the OSN is increased.