The dynamical structure of political corruption networks

Ribeiro, Haroldo V.; Alves, Luiz G. A.; Martins, Alvaro F.; Lenzi, E. K.; Perc, Matjaž

doi:10.1093/comnet/cny002

Cited by 86 publications

(78 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Internationally, large scale firm ownership data reveals how firms avoid taxes [41]. Crowdsourced data on convicted public officials, for instance extracted from Wikipedia, has been used to study the emergence of systemic corruption [18].…”

Section: Related Workmentioning

confidence: 99%

Corruption risk in contracting markets: a network science perspective

Wachs

Fazekas

Kertész³

2020

Int J Data Sci Anal

View full text Add to dashboard Cite

We use methods from network science to analyze corruption risk in a large administrative dataset of over 4 million public procurement contracts from European Union member states covering the years 2008-2016. By mapping procurement markets as bipartite networks of issuers and winners of contracts we can visualize and describe the distribution of corruption risk. We study the structure of these networks in each member state, identify their cores and find that highly centralized markets tend to have higher corruption risk. In all EU countries we analyze, corruption risk is significantly clustered. However, these risks are sometimes more prevalent in the core and sometimes in the periphery of the market, depending on the country. This suggests that the same level of corruption risk may have entirely different distributions. Our framework is both diagnostic and prescriptive: it roots out where corruption is likely to be prevalent in different markets and suggests that different anti-corruption policies are needed in different countries.Address correspondence to: johannes.wachs@cssh.rwth-aachen.de. This article is based on a chapter of the doctoral dissertation of Johannes Wachs.

show abstract

Section: Related Workmentioning

confidence: 99%

Corruption risk in contracting markets: a network science perspective

Wachs

Fazekas

Kertész³

2020

Int J Data Sci Anal

View full text Add to dashboard Cite

show abstract

“…The latter approach, (C), involves finding a partitioning of vertices {{1, 2, 3}, {4, 5}} (shown by green and purple colors in Fig. 1) which minimizes the total number of intra-group negative and inter-group positive edges to 1 (only edge (1,5) according to this partitioning). Note that removing edge (1,5) leads to a balanced signed graph.…”

Section: Evaluating Balance and Frustrationmentioning

confidence: 99%

“…We propose a general method for identifying cohesive groups in signed networks (networks with positive and negative edges), and apply it to political networks, which have become a common focus in complex network analysis [1][2][3] . Specifically, we examine signed networks of political collaboration and opposition to identify the members of polarized coalitions in the US Congress, then use these coalitions to examine the impact of polarization on effectiveness in passing bills.…”

mentioning

confidence: 99%

Detecting coalitions by optimally partitioning signed networks of political collaboration

Aref

Neal

2020

Sci Rep

View full text Add to dashboard Cite

We propose new mathematical programming models for optimal partitioning of a signed graph into cohesive groups. To demonstrate the approach's utility, we apply it to identify coalitions in US Congress since 1979 and examine the impact of polarized coalitions on the effectiveness of passing bills. Our models produce a globally optimal solution to the NP-hard problem of minimizing the total number of intra-group negative and inter-group positive edges. We tackle the intensive computations of dense signed networks by providing upper and lower bounds, then solving an optimization model which closes the gap between the two bounds and returns the optimal partitioning of vertices. our substantive findings suggest that the dominance of an ideologically homogeneous coalition (i.e. partisan polarization) can be a protective factor that enhances legislative effectiveness.

show abstract

“…More broadly, network methods have been fruitfully applied to problems in criminology including corruption [22,49,56], the mafia [3], and the evolution of criminal behavior in society [50].…”

Section: Introductionmentioning

confidence: 99%

A network approach to cartel detection in public auction markets

Wachs

Kertész²

2019

Sci Rep

View full text Add to dashboard Cite

Competing firms can increase profits by setting prices collectively, imposing significant costs on consumers. Such groups of firms are known as cartels and because this behavior is illegal, their operations are secretive and difficult to detect. Cartels feel a significant internal obstacle: members feel short-run incentives to cheat. Here we present a network-based framework to detect potential cartels in bidding markets based on the idea that the chance a group of firms can overcome this obstacle and sustain cooperation depends on the patterns of its interactions. We create a network of firms based on their co-bidding behavior, detect interacting groups, and measure their cohesion and exclusivity, two group-level features of their collective behavior. Applied to a market for school milk, our method detects a known cartel and calculates that it has high cohesion and exclusivity. In a comprehensive set of nearly 150,000 public contracts awarded by the Republic of Georgia from 2011 to 2016, detected groups with high cohesion and exclusivity are significantly more likely to display traditional markers of cartel behavior. We replicate this relationship between group topology and the emergence of cooperation in a simulation model. Our method presents a scalable, unsupervised method to find groups of firms in bidding markets ideally positioned to form lasting cartels.

show abstract

The dynamical structure of political corruption networks

Cited by 86 publications

References 51 publications

Corruption risk in contracting markets: a network science perspective

Corruption risk in contracting markets: a network science perspective

Detecting coalitions by optimally partitioning signed networks of political collaboration

A network approach to cartel detection in public auction markets

Contact Info

Product

Resources

About