Displacement and disconnection: the impact of violence on migration networks and highway traffic in Mexico, by M. Coscia & R. Gutiérrez-Romero, published in Spatial Economic Analysis.

This paper examines how violence impacts migration flows and the strength of migration networks across Mexico’s 2454 municipalities. Using a novel network algorithm and census data from 2005 to 2020, we detect structural changes in domestic and international migration beyond what net flows reveal. To identify causal effects, homicide rates are instrumented using variation in fuel prices and municipal distance to fuel pipelines, capturing exogenous shocks from large-scale fuel theft. Rising violence led to 1.12 million additional domestic emigrants, 50,200 fewer returnees from the United States, stronger emigration networks and reduced highway traffic linking violent areas to the rest of the country.

The trade-off between directness and coverage in transport network growth, by C. Sebastiao, A. Vybornova, A.R. Vierø, L.M. Aiello & M. Szell, published in Applied Network Science

We systematically study the growth of connected planar networks, quantifying functionality of the growing network structure. We compare random growth with various greedy and human-designed, manual growth strategies. We evaluate our results via the fundamental performance metrics of directness and coverage, finding non-trivial trade-offs between them. Manual strategies fare better than greedy strategies on both metrics, while random strategies perform worst and are unlikely to be Pareto efficient. Centrality-based greedy strategies tend to perform best for directness but are worse than random strategies for coverage, while coverage-based greedy strategies can achieve maximum global coverage as fast as possible but perform as poorly for directness as random strategies. Directness-based greedy strategies get stuck in local optimum traps. These results hold for a number of stylized urban transport network topologies. Our insights are crucial for applications where the order in which links are added to a spatial network is important, such as in urban or regional transport network design problems.

We have two new publications out!

1. Disconnect between the public face and the voting behavior of political representatives by Christian Ivert Andersen and Michele Coscia, published in the journal Applied Network Science.
   
   One of representative democracy’s tenets is that a political candidate runs on a specific platform, which is information the electorate uses to determine whether to vote for them or not. If this promise is to be maintained, it is fundamental that the public face candidates present corresponds to their actions in parliament once elected. Such a promise has been put in question both by scholars, but also by the electorate. In different countries at different times, the people have expressed various degrees of dissatisfaction with democracy: often the feeling is that representatives put their own interests—or the interest of a powerful minority—before the ones of their constituencies. In this paper, we propose a network-based quantitative investigation of this disconnect between the public face and the voting behavior of elected representatives. By using data from Denmark, we can place politicians in two different spaces, determined by their electoral campaign promises on the one hand, and on the other hand by the votes they cast in parliament. We find that our technique makes it possible both to find clear, expected, and consistent left-right divides between the political parties; as well as a larger-than-expected disconnect between the public face and the voting behavior. Our preliminary results indicate that the aggregate voting behavior in parliament of politicians does not match with how they present themselves to the public on the salient issues discussed during the election campaign.
2. Evaluating fraud detection algorithms in a decentralized scenario by Ada M Gige, Lasse Buschmann Alsbirk, Michele Coscia, published in the journal Royal Society Open Science.
   
   Financial fraud is an umbrella term including a vast number of illegal activities. These activities involve a significant fraction of the global economy. Traditional investigation techniques are labour-intensive and cannot scale to match the size of the issue. Machine learning has provided effective tools which deliver high accuracy in identifying transactions that could be involved in fraudulent activities. In this paper, we point out that the state-of-the-art in financial fraud detection has been applied to the unrealistic scenario of an omniscient centralized global authority which has access to all bank transactions globally. We propose a more realistic evaluation scenario, one made of two steps: first, the bank flags its own transactions using exclusively information it possesses; then only flagged transactions from all banks are analysed by the governmental authority for potential prosecution. We find that, in such a realistic scenario, the effectiveness of the state-of-the-art method for financial fraud detection decreases. Moreover, we show that in this decentralized scenario, it pays off to use simpler methods than the state-of-the-art, depending on the specific objective function the system wants to ensure.