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Adapting to Disruptions: Flexibility as a Pillar of Supply Chain Resilience
(2023)arXivSupply chain disruptions cause shortages of raw material and products. To increase resilience, i.e., the ability to cope with shocks, substituting goods in established supply chains can become an effective alternative to creating new distribution links. We demonstrate its impact on supply deficits through a detailed analysis of the US opioid distribution system. Reconstructing 40 billion empirical distribution paths, our data-driven model ...Working Paper -
The downside of heterogeneity: How established relations counteract systemic adaptivity in tasks assignments
(2021)arXivWe 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 ...Working Paper -
Struggling with change: The fragile resilience of collectives
(2022)arXivCollectives form non-equilibrium social structures characterised by a volatile dynamics. Individuals join or leave. Social relations change quickly. Therefore, differently from 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. ...Working Paper -
Predicting Sequences of Traversed Nodes in Graphs using Network Models with Multiple Higher Orders
(2020)arXivWe propose a novel sequence prediction method for sequential data capturing node traversals in graphs. Our method builds on a statistical modelling framework that combines multiple higher-order network models into a single multi-order model. We develop a technique to fit such multi-order models in empirical sequential data and to select the optimal maximum order. Our framework facilitates both next-element and full sequence prediction ...Working Paper -
Generalized Hypergeometric Ensembles: Statistical Hypothesis Testing in Complex Networks
(2016)arXivStatistical ensembles define probability spaces of all networks consistent with given aggregate statistics and have become instrumental in the analysis of relational data on networked systems. Their numerical and analytical study provides the foundation for the inference of topological patterns, the definition of network-analytic measures, as well as for model selection and statistical hypothesis testing. Contributing to the foundation ...Working Paper -
Probing the robustness of nested multi-layer networks
(2019)arXivWe consider a multi-layer network with two layers, $\mathcal{L}_{1}$, $\mathcal{L}_{2}$. Their intra-layer topology shows a scale-free degree distribution and a core-periphery structure. A nested structure describes the inter-layer topology, i.e., some nodes from $\mathcal{L}_{1}$, the generalists, have many links to nodes in $\mathcal{L}_{2}$, specialists only have a few. This structure is verified by analyzing two empirical networks ...Working Paper -
Improving the robustness of online social networks: A simulation approach of network interventions
(2019)arXivOnline 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 ...Working Paper -
Reconstructing signed relations from interaction data
(2022)arXivPositive 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 ...Working Paper -
Detecting Path Anomalies in Time Series Data on Networks
(2019)arXivThe unsupervised detection of anomalies in time series data has important applications, e.g., in user behavioural modelling, fraud detection, and cybersecurity. Anomaly detection has been extensively studied in categorical sequences, however we often have access to time series data that contain paths through networks. Examples include transaction sequences in financial networks, click streams of users in networks of cross-referenced ...Working Paper