Bureaucrats or Ideologues? EU Merger Control as Market‐Centred Integration

Since 1989, no major European merger has been able to go through without EU approval. The introduction of a centralized merger control procedure was another increase in the powers of the Commission’s Directorate‐General for Competition (DG COMP). While some see it playing a neo‐mercantilist role in a positive European integration, others underline its neoliberal ideological roots. Through our analysis of all merger decisions made between 1990 and 2016 (6,161 cases), we instead find evidence for market‐centred negative integration: DG COMP is particularly harsh towards coordinated market economies and targets sectors that have high levels of state intervention, thus thwarting the rise of ‘European champions’. Our interviews with merger experts and the decision citation data further suggest that this market‐centred logic of enforcement is not necessarily driven by ideology, but by the silent logic of bureaucratic autonomy. We thus contribute to the debate on the EU as a supranational force of economic liberalization.Introduction I Conceptual Framework II Data and Variables III Results IV Discussion: The Autonomy of DG COMB Conclusion Supporting Information References Interview

Relevance of SIR Model for Real-world Spreading Phenomena: Experiments on a Large-scale P2P System

Abstract—Understanding the spread of information on complex networks is a key issue from a theoretical and applied perspective. Despite the effort in developing theoretical models for this phenomenon, gauging them with large-scale real-world data remains an important challenge due to the scarcity of open, extensive and detailed data. In this paper, we explain how traces of peer-to-peer file sharing may be used to this goal. We also perform simulations to assess the relevance of the standard SIR model to mimic key properties of spreading cascade. We examine the impact of the network topology on observed properties and finally turn to the evaluation of two heterogeneous versions of the SIR model. We conclude that all the models tested failed to reproduce key properties of such cascades: typically real spreading cascades are relatively “elongated ” compared to simulated ones. We have also observed some interesting similarities common to all SIR models tested. I

Data-driven traffic and diffusion modeling in peer-to-peer networks: A real case study

Peer-to-peer (p2p) systems have driven a lot of attention in the past decade as they have become a major source of internet traffic. The amount of data flowing through the p2p network is huge and hence difficult both to comprehend and to control. In this work, we take advantage of a new and rich dataset recording p2p activity at a remarkable scale to give some answers to these difficult problems. After extracting the relevant and measurable properties of the network from the data, we develop two models that aim to make the link between the low-level properties of the network, such as the proportion of free-riders or the distribution of the files among the peers, and its highlevel properties, such as the Quality of Service or the diffusion characteristics, which are the interesting ones. We observe a nice agreement between the high-level properties measured on the real data and on the data simulated by our models, which is encouraging for our models to be used in practice as large-scale prediction tools. Using our models, we make a first prediction and show that it is worth spending efforts to reduce the amount of free-riders to improve the availability of files on the network, but only down to about 65% of free-riders

Comparing Overlapping Properties of Real Bipartite Networks

International audienceMany real-world networks lend themselves to the use of graphs for analysing and modelling their structure. But such a simple representation has proven to miss some important and non trivial properties hidden in the bipartite structure of the networks. Recent papers have shown that overlapping properties seem to be present in bipartite networks and that it could explain better the properties observed in simple graphs. This work intends to investigate this question by studying two proposed metrics to account for overlapping structures in bipartite networks. The study, conducted on four dataset stemming from very different contexts (computer science, juridical science and social science), shows that the most popular metrics, the clustering coefficient, turns out to be less relevant that the recent redundancy coefficient to analyse intricate overlapping properties of real networks

Combinatorial optimization problems with conflict graphs

Conflict graphs impose disjunctive constraints for pairs of jobs, items, edges or other objects in a combinatorial optimization problem. Equivalently, the feasible domain of the considered problem is restricted to stable sets in the given conflict graph. After reviewing in our presentation results from the literature for bin packing and scheduling problems with conflict graphs, we first consider the classical 0-1 knapsack problem. Adding a conflict graph makes the problem strongly NP-hard but for three special graph classes, namely trees, graphs with bounded treewidth and chordal graphs, we can develop pseudopolynomial algorithms. From these we can easily derive fully polynomial time approximation schemes (FPTAS). Secondly, we study the minimum spanning tree problem and show that the border between polynomially solvable and NP-hard is given by moving from a conflict graph containing only isolated edges to paths of length 2