The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts

In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional content in the production of automotive components and parts traded between the three partner countries, United States, Canada and Mexico. Signed by all three countries, the new trade agreement, USMCA, is to go into force in 2022. Nonetheless, after the 2020 Presidential election, the new treaty's future is under discussion, and its impact on the automotive industry is not entirely defined. Another significant shift in this industry – the accelerated rise of electric vehicles – also occurred in 2020: while the COVID-19 pandemic largely halted most plants in the automotive value chain all over the world, at the reopening, the tide is now running against internal combustion engine vehicles, at least in the announcements and in some large investments planned in Europe, Asia and the US. The definition of the pre-pandemic situation is a very helpful starting point for the analysis of the possible repercussions of the technological and geo-political transition, which has been accelerated by the epidemic, on geographical clusters and sectorial spe-cialisations of the main regions and countries. This paper analyses the trade networks emerging in the past 25 years out of this turmoil in a new analytical framework. In the economic literature on international trade, the study of the automotive global value chains has been addressed by using network analysis, focusing on the centrality of geographical regions and countries while largely overlooking the contribution of countries' bilateral trading in components and parts as structuring forces the subnetwork of coun-tries and their specific situations in the overall trade network. This paper focuses on such subnetworks as meso-level structures emerging in trade network over the last 25 years. Using the Infomap multilayer clustering algorithm, we are able to identify clusters of countries and their specific trades in the automotive international trade network and to highlight the relative importance of each cluster, the interconnections between them, and to analyse the contribution of countries and of components and parts in the clusters. We draw the data from the UN Comtrade database of directed export and import flows of 30 automotive components and parts among 42 countries (accounting for 98% of world trade flows of those items). The paper highlights the changes that occurred over 25 years in the geography of the trade relations, with particular with regard to denser and more hierarchical network generated by Germany’s trade relations within EU countries and by the US preferential trade agreements with Canada and Mexico, and the upsurge of China. With a similar overall variety of traded components and parts within the main clusters (dominated respectively by Germany, US and Japan-China), the Infomap multilayer analysis singles out which components and parts determined the relative positions of countries in the various clusters and the changes over time in the relative positions of countries and their specialisations in multilateral trades. Connections between clusters increase over time, while the relative importance of the main clusters and of some individual countries change significantly. The focus on US and Mexico and on Germany and Central Eastern European countries (Czech Republic, Hungary, Poland, Slovakia) will drive the comparative analysis

Skeleton coupling: a novel interlayer mapping of community evolution in temporal networks

Dynamic community detection (DCD) in temporal networks is a complicated task that involves the selection of an algorithm and its associated parameters. How to choose the most appropriate algorithm generally depends on the type of network being analyzed and the specific properties of the data that define the network. In functional temporal networks derived from neuronal spike train data, communities are expected to be transient, and it is common for the network to contain multiple singleton communities. Here, we compare the performance of different DCD algorithms on functional temporal networks built from synthetic neuronal time series data with known community structure. We find that, for these networks, DCD algorithms that utilize interlayer links to perform community carryover between layers outperform other methods. However, we also observe that algorithm performance is highly dependent on the topology of interlayer links, especially in the presence of singleton and transient communities. We therefore define a novel method for defining interlayer links in temporal networks called skeleton coupling that is specifically designed to enhance the linkage of communities in the network throughout time based on the topological properties of the community history. We show that integrating skeleton coupling with current DCD methods improves algorithm performance in synthetic data with planted singleton and transient communities. The use of skeleton coupling to perform DCD will therefore allow for more accurate and interpretable results of community evolution in real-world neuronal data or in other systems with transient structure and singleton communities.Comment: 19 pages, 8 figure

Regionalisation and cross-region integration. Twin dynamics in the automotive international trade networks

The paper analyses the changes that occurred over 25 years in the geography of trade in automotive parts and components. Using the Infomap multilayer clustering algorithm, we identify clusters of countries and their specific trades in the automotive international trade network, we measure the relative importance of each cluster and the interconnections between them, and we analyse the contribution of countries and of trade of components and parts in the clusters. The analysis highlights the formation of denser and more hierarchical networks generated by Germany's trade relations with EU countries and by the US preferential trade agreements with Canada and Mexico, as well as the surge of China. While the relative importance of the main clusters and of some individual countries change significantly, connections between clusters increase over time

Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management

An analytic strategy for data processing of multimode networks

Complex network data structures are considered to capture the richness of social phenomena and real-life data settings. Multipartite networks are an example in which various scenarios are represented by different types of relations, actors, or modes. Within this context, the present contribution aims at discussing an analytic strategy for simplifying multipartite networks in which different sets of nodes are linked. By considering the connection of multimode networks and hypergraphs as theoretical concepts, a three-step procedure is introduced to simplify, normalize, and filter network data structures. Thus, a model-based approach is introduced for derived bipartite weighted networks in order to extract statistically significant links. The usefulness of the strategy is demonstrated in handling two application fields, that is, intranational student mobility in higher education and research collaboration in European framework programs. Finally, both examples are explored using community detection algorithms to determine the presence of groups by mixing up different modes

Language comparison via network topology

Modeling relations between languages can offer understanding of language characteristics and uncover similarities and differences between languages. Automated methods applied to large textual corpora can be seen as opportunities for novel statistical studies of language development over time, as well as for improving cross-lingual natural language processing techniques. In this work, we first propose how to represent textual data as a directed, weighted network by the text2net algorithm. We next explore how various fast, network-topological metrics, such as network community structure, can be used for cross-lingual comparisons. In our experiments, we employ eight different network topology metrics, and empirically showcase on a parallel corpus, how the methods can be used for modeling the relations between nine selected languages. We demonstrate that the proposed method scales to large corpora consisting of hundreds of thousands of aligned sentences on an of-the-shelf laptop. We observe that on the one hand properties such as communities, capture some of the known differences between the languages, while others can be seen as novel opportunities for linguistic studies

Understanding Complex Systems: From Networks to Optimal Higher-Order Models

To better understand the structure and function of complex systems, researchers often represent direct interactions between components in complex systems with networks, assuming that indirect influence between distant components can be modelled by paths. Such network models assume that actual paths are memoryless. That is, the way a path continues as it passes through a node does not depend on where it came from. Recent studies of data on actual paths in complex systems question this assumption and instead indicate that memory in paths does have considerable impact on central methods in network science. A growing research community working with so-called higher-order network models addresses this issue, seeking to take advantage of information that conventional network representations disregard. Here we summarise the progress in this area and outline remaining challenges calling for more research.Comment: 8 pages, 4 figure