Detecting the functional interaction structure of software development teams.

The functional interaction structure of a team captures the preferences with which members of different roles interact. This paper presents a data-driven approach to detect the functional interaction structure for software development teams from traces team members leave on development platforms during their daily work. Our approach considers differences in the activity levels of team members and uses a block-constrained configuration model to compute interaction preferences between members of different roles.

Tracing Opioids Across the US: A High-Resolution Pharmaceutical Distribution Dataset.

In the field of pharmaceutical supply chains, there is a lack of comprehensive historical data, representing a significant barrier to advancing research. To address this gap, we introduce a high-resolution dataset comprising drug packages distributed to approximately 300,000 pharmacies, hospitals, and practitioners across the US. We reconstruct 375 million distribution paths from ARCOS, a DEA-maintained database comprising half a billion shipping records between 2006 and 2014.

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.

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.

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 network representations of software teams' interaction structures.

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.

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.

Consensus from group interactions: An adaptive voter model on hypergraphs.

We study the effect of group interactions on the emergence of consensus in a spin system. Agents with discrete opinions {0,1} form groups. They can change their opinion based on their group's influence (voter dynamics), but groups can also split and merge (adaptation).

Social nucleation: Group formation as a phase transition.

The spontaneous formation and subsequent growth, dissolution, merger, and competition of social groups bears similarities to physical phase transitions in metastable finite systems. We examine three different scenarios, percolation, spinodal decomposition, and nucleation, to describe the formation of social groups of varying size and density. In our agent-based model, we use a feedback between the opinions of agents and their ability to establish links.

Data-driven modelling of group formation in the fission-fusion dynamics of Bechstein's bats.

Communal roosting in Bechstein's bat colonies is characterized by the formation of several groups that use different day roosts and that regularly dissolve and re-merge (fission-fusion dynamics). Analysing data from two colonies of different sizes over many years, we find that (i) the number of days that bats stay in the same roost before changing follows an exponential distribution that is independent of the colony size and (ii) the number and size of groups that bats formed for roosting depend on the size of the colony, such that above a critical colony size two to six groups of different sizes are formed. To model these two observations, we propose an agent-based model in which agents make their decisions about roosts based on both random and social influences.

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.

Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net.

Unlabelled: Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g.

Quantifying the Importance of Firms by Means of Reputation and Network Control.

As recently argued in the literature, the reputation of firms can be channeled through their ownership structure. We use this relation to model reputation spillovers between transnational companies and their participated companies in an ownership network core of 1,318 firms. We then apply concepts of network controllability to identify minimum sets of driver nodes (MDSs) of 314 firms in this network.

Reproducing scientists' mobility: a data-driven model.

High skill labour is an important factor underpinning the competitive advantage of modern economies. Therefore, attracting and retaining scientists has become a major concern for migration policy. In this work, we study the migration of scientists on a global scale, by combining two large data sets covering the publications of 3.

Quantifying individual influence in leading-following behavior of Bechstein's bats.

Leading-following behavior as a way of transferring information about the location of resources is wide-spread in many animal societies. It represents active information transfer that allows a given social species to reach collective decisions in the presence of limited information. Although leading-following behavior has received much scientific interest in the form of field studies, there is a need for systematic methods to quantify and study the individual contributions in this information transfer, which would eventually lead us to hypotheses about the individual mechanisms underlying this behaviour.

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.

Modeling User Reputation in Online Social Networks: The Role of Costs, Benefits, and Reciprocity.

We analyze an agent-based model to estimate how the costs and benefits of users in an online social network (OSN) impact the robustness of the OSN. Benefits are measured in terms of relative reputation that users receive from their followers. They can be increased by direct and indirect reciprocity in following each other, which leads to a core-periphery structure of the OSN.

Multilayer network approach to modeling authorship influence on citation dynamics in physics journals.

We provide a general framework to model the growth of networks consisting of different coupled layers. Our aim is to estimate the impact of one such layer on the dynamics of the others. As an application, we study a scientometric network, where one layer consists of publications as nodes and citations as links, whereas the second layer represents the authors.

An agent-based model of multi-dimensional opinion dynamics and opinion alignment.

It is known that individual opinions on different policy issues often align to a dominant ideological dimension (e.g., left vs right) and become increasingly polarized.

The interdependence of corporate reputation and ownership: a network approach to quantify reputation.

We propose a novel way to measure the reputation of firms by using information about their ownership structure. Supported by the signalling theory, we argue that ownership relations channel reputation spillovers between shareholders and their invested companies. We model such reputation spillovers by means of a simple dynamics that runs on the ownership network, constructed from available databases.

Correlations between thresholds and degrees: An analytic approach to model attacks and failure cascades.

Two node variables determine the evolution of cascades in random networks: a node's degree and threshold. Correlations between both fundamentally change the robustness of a network, yet they are disregarded in standard analytic methods as local tree or heterogeneous mean field approximations, since order statistics are difficult to capture analytically because of their combinatorial nature. We show how they become tractable in the thermodynamic limit of infinite network size.

Framework for cascade size calculations on random networks.

We present a framework to calculate the cascade size evolution for a large class of cascade models on random network ensembles in the limit of infinite network size. Our method is exact and applies to network ensembles with almost arbitrary degree distribution, degree-degree correlations, and, in case of threshold models, for arbitrary threshold distribution. With our approach, we shift the perspective from the known branching process approximations to the iterative update of suitable probability distributions.

Explicit size distributions of failure cascades redefine systemic risk on finite networks.

How big is the risk that a few initial failures of nodes in a network amplify to large cascades that span a substantial share of all nodes? Predicting the final cascade size is critical to ensure the functioning of a system as a whole. Yet, this task is hampered by uncertain and missing information. In infinitely large networks, the average cascade size can often be estimated by approaches building on local tree and mean field approximations.

Quantifying the effect of editor-author relations on manuscript handling times.

In this article we study to what extent the academic peer review process is influenced by social relations between the authors of a manuscript and the editor handling the manuscript. Taking the open access journal PlosOne as a case study, our analysis is based on a data set of more than 100,000 articles published between 2007 and 2015. Using available data on handling editor, submission and acceptance time of manuscripts, we study the question whether co-authorship relations between authors and the handling editor affect the , i.

The dynamics of emotions in online interaction.

We study the changes in emotional states induced by reading and participating in online discussions, empirically testing a computational model of online emotional interaction. Using principles of dynamical systems, we quantify changes in valence and arousal through subjective reports, as recorded in three independent studies including 207 participants (110 female). In the context of online discussions, the dynamics of valence and arousal is composed of two forces: an internal relaxation towards baseline values independent of the emotional charge of the discussion and a driving force of emotional states that depends on the content of the discussion.