There are many studies exploring the factors that determine trade between countries. However, little is known about small countries, where the size of the country, as well as geographical and socio-economic specifics, may significantly influence the principles of collaboration. In our work, we examine the features and patterns of collaboration among small post-Soviet countries. Using the BACI-CEPII dataset from 2021, we analyze the trade value of these countries, their trade specialization, and the factors that may determine it. Trade specialization is assessed using the UNCTAD methodology, which distinguishes between primary sector goods and manufactured products. To identify the specific factors influencing trade, we construct a weighted directed network of their trade relationships. We apply Generalized Exponential Random Graph Modeling (GERGM) to uncover the factors determining this trade. We investigate the following factors: geographical proximity, WTO membership, differences in trade specialization structures, and GDP per capita. Our findings reveal that trade among post-Soviet countries, both within the group and with other nations, differs significantly. These differences are evident in trade intensity and specialization. Our preliminary results suggest that geographical proximity and economic characteristics are significant determinants of trade. Our results contribute to a deeper understanding of the unique interactions between small post-Soviet states and explore whether differences exist in their cooperation compared to the rest of the world.

Sanctions have long been an essential foreign policy tool, used by governments, international and regional organizations to signal discontent, enforce international norms or pressure actors into changing their behavior. Over time, the debate around their use and effectiveness has evolved considerably, reflecting shifts in international dynamics and the discussion around the grave humanitarian impacts of sanctions. To counter or pressure the actions of autocratic regimes, the USA and EU use a set of different sanction measures aimed at limiting the support for the regime and its capacity to repress. Targeted sanctions against individuals aim to restrict the actions of those directly accountable, thereby reducing the risk of collateral damage. Despite the depth of the existing literature, little is known about their micro-level impact on individual actors and their social networks. This paper addresses this gap by employing undirected Stochastic Actor-Oriented Models (SAOM) to analyze how targeted sanctions affect individual positions within dynamic political and economic networks. Using a new, high-resolution dataset that captures monthly sanction interventions by the USA and EU against individuals, the paper explores whether sanctioning actors effectively disrupts their network structures or merely triggers adaptation mechanisms. The paper hypothesizes that sanctioned individuals operate in resilient, highly structured networks where strategic ties allow them to circumvent restrictions and maintain influence despite external pressure. By leveraging SAOM, the paper examines the longitudinal evolution of individual actor behaviors within these networks, identifying how sanctioned actors adjust their alliances, centrality, and relational choices over time while also disentangling the processes of tie formation and dissolution, shedding light on whether sanctions result in meaningful fragmentation or simply a reconfiguration of network ties. This integration of SOAM for undirected networks with empirical sanction data provides a more nuanced understanding of how external interventions shape social structures over time. The analysis will help policymakers assess the efficiency and potential outcomes of sanctions, making it easier to determine when and how they can be most effectively applied to achieve desired political goals.

This presentation introduces a novel approach to analyzing transportation networks by incorporating multiple levels of changing administrative boundaries. Using Berlin's transportation system (1945-1989) as a case study, we demonstrate how this method enables analysis of network evolution in relation to demographic changes at different administrative scales.

Drawing on historical timetables (Fahrplanbücher) and administrative records, we construct detailed network snapshots that capture the full public transportation system, including subway, rail, tram, and bus services. A key challenge addressed is the dynamic nature of Berlin's administrative boundaries during this period, requiring a model that can track both network evolution and territorial reorganization. We implement this as a multilayer network in Neo4j, allowing us to model and query complex relationships between different types of nodes (stations, administrative units, temporal markers) as they change over time.

Our methodological contribution is threefold. First, we present a data model that maintains relationships between transportation nodes (stations) and multiple, temporally-aware administrative boundaries (districts, neighborhoods, and postal codes). Second, we introduce techniques for analyzing network metrics aggregated at different administrative levels, allowing integration with historical demographic data. Third, we demonstrate visualization methods developed for a web application that enables interactive exploration of how transportation infrastructure evolved within these changing administrative contexts.

This approach reveals new insights into the relationship between transportation infrastructure development and population dynamics at the district level, while accounting for Berlin's unique historical context as a divided city. Our methodology is particularly valuable for researchers studying how transportation networks evolve within changing administrative frameworks, offering tools for both analysis and visualization of complex spatial-temporal relationships.

The discourse surrounding environmental and climate change has evolved significantly in the political sphere, recognizing climate change as a pressing political issue that transcends partisanship. This is marked by a growing consensus on its urgency.

In Japan, the Kishida Cabinet (2021 - 2024) has continued the climate policies of its predecessors, maintaining ambitious targets for reducing greenhouse gas emissions and achieving carbon neutrality. Their hybrid strategy incorporates both traditional and emerging energy sources alongside a reevaluation of nuclear power and proposals for advanced reactors with the initiatives aiming at public and private investments. However, the cabinet has faced criticism for its reliance on prospective future technologies, which diverts focus from immediate climate action. Additionally, the goals and policies of the cabinet are perceived as lacking in detail and accountability. The technocratic methods adopted by ministries, along with Japan’s traditional central governance structure, present further challenges to practical implementation.

This research delves into the legislative cosponsorship networks within the environmental and energy sectors in the House of Representatives during the Kishida cabinet’s tenure. It analyzes the dynamics among legislators influenced by political parties, factions, regions, areas of expertise, and the roles of various councils and committees. Utilizing descriptive and statistical approaches within specific timeframes, the study considers the impact of the cabinet on policy direction and bill sponsorship. Preliminary findings reveal that both homophily and heterophily significantly influence the legislative cosponsorship networks in Japan, with various factors shaping these dynamics in the policymaking process.