TY - RPRT A1 - Stephany, Fabian A1 - Stoehr, Niklas A1 - Darius, Philipp A1 - Neuhäuser, Leonie A1 - Teutloff, Ole A1 - Braesemann, Fabian T1 - The CoRisk-Index: A data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time T2 - General Economics (econ.GN) N2 - While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such policy measures, it is key to identify the industries that are most in need of support. In this study, we introduce a data-mining approach to measure industry-specific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries. The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is updated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis. KW - COVID-19 KW - Coronavirus KW - Economic risk KW - Risk reports KW - SEC filings Y1 - 2020 UR - https://arxiv.org/abs/2003.12432v3 ER - TY - JOUR A1 - Neuhäuser, Leonie A1 - Mellor, Andrew A1 - Lambiotte, Renaud T1 - Multi-body Interactions and Non-Linear Consensus Dynamics on Networked Systems JF - Physical Review E N2 - Multi-body interactions can reveal higher-order dynamical effects that are not captured by traditional two-body network models. In this work, we derive and analyse models for consensus dynamics on hypergraphs, where nodes interact in groups rather than in pairs. Our work reveals that multi-body dynamical effects that go beyond rescaled pairwise interactions can only appear if the interaction function is non-linear, regardless of the underlying multi-body structure. As a practical application, we introduce a specific non-linear function to model three-body consensus, which incorporates reinforcing group effects such as peer pressure. Unlike consensus processes on networks, we find that the resulting dynamics can cause shifts away from the average system state. The nature of these shifts depends on a complex interplay between the distribution of the initial states, the underlying structure and the form of the interaction function. By considering modular hypergraphs, we discover state-dependent, asymmetric dynamics between polarised clusters where multi-body interactions make one cluster dominate the other. KW - Collective behavior in networks KW - Community structure KW - Patterns in complex systems KW - Spreading Y1 - 2020 U6 - https://doi.org/10.1103/PhysRevE.101.032310 SN - 2470-0053 VL - 101 IS - 3 ER - TY - RPRT A1 - Sahasrabuddhe, Rohit A1 - Neuhäuser, Leonie A1 - Lambiotte, Renaud T1 - Modelling Non-Linear Consensus Dynamics on Hypergraphs T2 - Physics and Society N2 - The basic interaction unit of many dynamical systems involves more than two nodes. In such situations where networks are not an appropriate modelling framework, it has recently become increasingly popular to turn to higher-order models, including hypergraphs. In this paper, we explore the non-linear dynamics of consensus on hypergraphs, allowing for interactions within hyperedges of any cardinality. After discussing the different ways in which non-linearities can be incorporated in the dynamical model, building on different sociological theories, we explore its mathematical properties and perform simulations to investigate them numerically. After focussing on synthetic hypergraphs, namely on block hypergraphs, we investigate the dynamics on real-world structures, and explore in detail the role of involvement and stubbornness on polarisation. Y1 - 2020 UR - https://arxiv.org/abs/2007.09391 ET - arXiv:2007.09391v1 [physics.soc-ph] 18 Jul 2020 ER - TY - RPRT A1 - Stamm, Felix I. A1 - Neuhäuser, Leonie A1 - Lemmerich, Florian A1 - Schaub, Michael T. A1 - Strohmaier, Markus T1 - Systematic edge uncertainty in attributed social networks and its effects on rankings of minority nodes T2 - Social and Information Networks N2 - Network analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered data is error-free and reliable. Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community. In this paper, we model how such systematic uncertainty on edges of an attributed network can impact network analysis, in particular the ranking of nodes. We discuss how erroneous edge observations can be driven by external node attributes and the relative edge positions in the network, thereby opening a path towards a systematic study of the effects of edge-uncertainty for various network analysis tasks. To show how conclusions drawn from network analyses can get distorted due to such inaccuracies, we focus on the effects of edge-uncertainty on minority group representations in degree-based rankings. For that purpose, we analyze synthetic and real networks with varying homophily and group sizes. We find that introducing edge uncertainty can significantly alter the relative density of networks and result both in a strongly increased or decreased ranking of the minority, depending on the type of edge error and homophily. Our model enables researchers to include systematic edge-uncertainty in their analyses and thereby better account for the role of minorities in social networks. Y1 - 2020 UR - https://arxiv.org/abs/2010.11546 ET - arXiv:2010.11546 [cs.SI] ER -