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Probing the robustness of nested multilayer networks
(2019)arXivWe consider a multilayer network with two layers, $\mathcal{L}_{1}$, $\mathcal{L}_{2}$. Their intralayer topology shows a scalefree degree distribution and a coreperiphery structure. A nested structure describes the interlayer 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 
The network structure of cityfirm relations
(2015)arXivHow are economic activities linked to geographic locations? To answer this question, we use a datadriven approach that builds on the information about location, ownership and economic activities of the world's 3,000 largest firms and their almost one million subsidiaries. From this information we generate a bipartite network of cities linked to economic activities. Analysing the structure of this network, we find striking similarities ...Working Paper 
The spatial component of R&D networks
(2015)arXivWe study the role of geography in R&D networks by means of a quantitative, microgeographic approach. Using a large database that covers international R&D collaborations from 1984 to 2009, we localize each actor precisely in space through its latitude and longitude. This allows us to analyze the R&D network at all geographic scales simultaneously. Our empirical results show that despite the high importance of the city level, transnational ...Working Paper 
How Damage Diversification Can Reduce Systemic Risk
(2015)arXivWe consider the problem of risk diversification in complex networks. Nodes represent e.g. financial actors, whereas weighted links represent e.g. financial obligations (credits/debts). Each node has a risk to fail because of losses resulting from defaulting neighbors, which may lead to large failure cascades. Classical risk diversification strategies usually neglect network effects and therefore suggest that risk can be reduced if possible ...Working Paper 
The value of peripheral nodes in controlling multilayer networks
(2015)arXivWe analyze the controllability of a twolayer network, where driver nodes can be chosen only from one layer. Each layer contains a scalefree network with directed links. The dynamics of nodes depends on the incoming links from other nodes (reputation dynamics). We find that the controllable part of the network is larger when choosing peripherial nodes to connect the two layers. The control is as efficient for peripherial nodes as driver ...Working Paper 
An ensemble perspective on multilayer networks
(2015)arXivWe study properties of multilayered, interconnected networks from an ensemble perspective, i.e. we analyze ensembles of multilayer networks that share similar aggregate characteristics. Using a diffusive process that evolves on a multilayer network, we analyze how the speed of diffusion depends on the aggregate characteristics of both intra and interlayer connectivity. Through a blockmatrix model representing the distinct layers, ...Working Paper 
Selection rules in alliance formation
(2014)arXivWe study how firms select partners using a large database of publicly announced R&D alliances over a period of 25 years. We identify, for the first time, two distinct behavioral strategies of firms in forming these alliances. By reconstructing and analysing the temporal R&D network of 14,000 international firms and 21.000 publicly announced alliances, we find a "universal" behavior in firms changing between these strategies. In the first ...Working Paper 
Newcomers vs. incumbents: How firms select their partners for R&D collaborations
(2014)arXivThis paper studies the selection of partners for R&D collaborations of firms both empirically, by analyzing a large data set of R&D alliances over 25 years, and theoretically, by utilizing an agentbased model of alliance formation. We quantify the topological position of a firm in the R&D network by means of the weighted kcore decomposition which assigns a coreness value to each firm. The evolution of these coreness values over time ...Working Paper 
SlowDown vs. SpeedUp of Information Diffusion in NonMarkovian Temporal Networks
(2013)We study the slowdown and speedup of diffusion in temporal networks with nonMarkovian contact sequences. We introduce a causalitypreserving timeaggregated representation that allows to analyze temporal networks from the perspective of spectral graph theory. With this we provide an analytical explanation for the frequently observed slowdown of diffusion in empirical nonMarkovian temporal networks. We derive an analytical prediction ...Working Paper 
The Rise and Fall of R&D Networks
(2013)Drawing on a large database of publicly announced R&D alliances, we track the evolution of R&D networks in a large number of economic sectors over a long time period (19862009). Our main goal is to evaluate temporal and sectoral robustness of the main statistical properties of empirical R&D networks. We study a large set of indicators, thus providing a more complete description of R&D networks with respect to the existing literature. We ...Working Paper