Triadic balance and network evolution in predictive models of signed networks

Understanding the impact of engineering design on product competitions is imperative for product designers to better address customer needs and develop more competitive products. In this paper, we propose a dynamic network-based approach for modeling and analyzing the evolution of product competitions using multi-year buyer survey data. The product co-consideration network, formed based on the likelihood of two products being co-considered from survey data, is treated as a proxy of products’ competition relations in a market. The separate temporal exponential random graph model (STERGM) is employed as the dynamic network modeling technique to model the evolution of network as two separate processes: link formation and link dissolution. We use China’s automotive market as a case study to illustrate the implementation of the proposed approach and the benefits of dynamic network models compared to the static network modeling approach based on an exponential random graph model (ERGM). The results show that since STERGM takes preexisting competition relations into account, it provides a pathway to gain insights into why a product may maintain or lose its competitiveness over time. These driving factors include both product attributes (e.g., fuel consumption) as well as current market structures (e.g., the centralization effect). With the proposed dynamic network-based approach, the insights gained from this paper can help designers better interpret the temporal changes of product competition relations to support product design decisions.

Biotechnology has significant potential in public health, offering critical support for communicable disease control, chronic illness management, and drug development. To foster biotechnology innovation, governments increasingly incentivize cooperations among organizations, resulting in more interconnected biotechnology cooperation networks. However, research on the evolution of these networks rely primarily on static network analysis and neglect the micromechanisms under the evolution, which lead to deviations in policymaking. Using temporal exponential random graph model (TERGM), which accounts for dynamic network correlations, and based on micromechanisms framework consisting of agency, opportunity and inertia, this study analyzes the impacts of both endogenous and exogenous factors on the evolution of biotechnology cooperation networks. The empirical analysis based on China's biotechnology patent data from 2004 to 2023 reveals the following findings and policy recommendations. First, the evolution of the biotechnology cooperation networks is temporally dependent, highlighting the need for awareness of policy lags. Second, two endogenous factors - transitivity and convergence - emerge in the evolution, implying the need for government to create information platforms, establish targeted project subsidies, and enforce technical confidentiality policies. Finally, with regard to exogenous factors, the networks exhibit geographical homogeneity, implying the needs for government to promote cross-regional cooperation by establishing innovation centers and unified standards to mitigate lock-in effects and barriers.

This paper assesses whether cross-border M&A decisions exhibit network effects. We estimate exponential random graph models (ERGM) and temporal exponential random graph models (TERGM) to evaluate the determinants of cross-country M&A investments at the sectoral level. The results show that transitivity matters: a country is more likely to invest in a new destination if one of its existing partners has already made some investments there. In line with the literature on export platforms and informational barriers, we find a sizable impact of third country effects on the creation of new investments. This effect is sizable and larger than some of the more traditional M&A determinants, such as trade openness.

Performance evaluation method for network monitoring based on separable temporal exponential random graph models with application to the study of autocorrelation effects

Analyzing the driving factors and mechanisms of urban carbon emission correlation networks can provide effective carbon reduction decision-making support for Shandong Province and other regions with similar industrial characteristics. Based on industrial carbon emission data from various cities in Shandong Province from 2013 to 2021, the spatial correlation network of carbon emission was established by using a modified gravity model. The characteristics of the network were explored by using the Social Network Analysis (SNA) method, and significant factors affecting the network were identified through Quadratic Assignment Procedure (QAP) correlation analysis and motif analysis. The driving mechanism of the carbon emission correlation network was analyzed by using Temporal Exponential Random Graph Models (TERGMs). The results show that: (1) The spatial correlation network of urban carbon emission in Shandong Province exhibits multi-threaded complex network correlations with a relatively stable structure, overcoming geographical distance limitations. (2) Qingdao, Jinan, and Rizhao have high degree centrality, betweenness centrality, and closeness centrality in the network, with Qingdao and Jinan being relatively central. (3) Shandong Province can be spatially clustered into four regions, each with distinct roles, displaying a certain “neighboring clustering” phenomenon. (4) Endogenous network structures such as Mutual, Ctriple, and Gwesp significantly impact the formation and evolution of the network, while Twopath does not show the expected impact; FDI can promote the generation of carbon emission reception relationships in the spatial correlation network; IR can promote the generation of carbon emission spillover relationships in the spatial correlation network; GS, differences in GDP, differences in EI, and similarities of IR can promote the generation of organic correlations within the network; on the temporal level, the spatial correlation network of urban carbon emission in Shandong Province has shown significant stability during the study period.

We propose a network-based theory of alliance formation. Our theory implies that, in addition to key state and dyad attributes already established in the literature, the evolution of the alliance network from any given point in time is largely determined by its structure. Specifically, we argue that closed triangles in the alliance network—where i is allied with j is allied with k is allied with i — produce synergy effects in which state-level utility is greater than the sum of its dyadic parts. This idea can be generalized to n-state closure, and, when considered along with factors that make dyadic alliance formation more attractive, such as military prowess and political compatibility, suggests that the network will evolve toward a state of several densely connected clusters of states with star-like groupings of states as an intermediary stage. To evaluate our theory, we use the temporal exponential random graph model and find that the roles of our network effects are robustly supported by the data, whereas the effects of non-network parameters vary substantially between periods of recent history. Our results indicate that network structure plays a greater role in the formation of alliance ties than has been previously understood in the literature.

This study, based on Complex Adaptive Systems theory and the "4C" framework, explores the dynamics of information sharing and collaboration networks within China's emergency management system during disasters. It rigorously explores the nuances in the connections and differences between these networks. Employing Social Network Analysis (SNA) and Temporal Exponential Random Graph Models (TERGMs), the research scrutinizes the relationships of disaster information sharing and collaboration among local public departments in the aftermath of the 2016 Funing tornado in Jiangsu, China. This study is dedicated to understanding how these networks evolve within a hierarchical administrative framework. The findings underscore three pivotal trends in the evolution of information and collaboration networks: a reduction in network redundancy, localized strengthening in ties, and differential adaptations. These trends are instrumental in enhancing the broader understanding of emergency management. They spotlight the importance of efficient information dissemination and robust collaborative frameworks, particularly in the context of China's centralized and hierarchical emergency management structure.

Social media networks are dynamic. As such, the order in which network ties develop is an important aspect of the network dynamics.This study proposes a novel dynamic network model, the Nodal Attribute-based Temporal Exponential Random Graph Model (NATERGM) for dynamic network analysis. The proposed model focuses on how the nodal attributes of a network affect the order in which the network ties develop. Empirical results showed that the NATERGM demonstrated an enhanced pattern testing capability compared to benchmark models. The proposed NATERGM model helps explain the roles of nodal attributes in the formation process of dynamic networks.

A Boon or a Bane? An Examination of Social Communication in Social Trading

Libya is the African country with the highest proportion of migrants and it is the major migration gateway to Europe. Since the fall of Gaddafi’s regime in 2011, Libya has been experiencing a prolonged period of instability and conflict which has also affected migration flows. This paper provides the first analytical study of the networks of irregular migrants toward, within, and from Libya since the beginning of the Second Civil War in 2014. We use longitudinal-geolocalised data on migrants’ movements and combine tools taken from spatial statistics and network analysis to reconstruct the network of migrants’ international movements towards and from Libya, and the network of migrants’ internal movements within Libya. Employing the information obtained from these networks, we map the different migrant routes within the country, we provide evidence that migrants sort into routes according to their nationality, and we show how these routes have evolved over time. Moreover, using a Separable Temporal Exponential Random Graph (STERG) model, we document that migrants are more likely to move towards areas with higher economic opportunities, lower level of conflict intensity, more same country-of-origin migrants, and connecting to multiple routes. Our analysis shows how to combine migration data with insights from spatial statistics and network analysis in order to reconstruct migrants’ movements.

Graph processes that unfold in continuous time are of obvious theoretical and practical interest. Particularly useful are those whose long-term behavior converges to a graph distribution of known form. Here, we review some of the conditions for such convergence, and provide examples of novel and/or known processes that do so. These include subfamilies of the well-known stochastic actor-oriented models, as well as continuum extensions of temporal and separable temporal exponential family random graph models. We also comment on some related threads in the broader work on network dynamics, which provide additional context for the continuous time case.

Improving sustainable performance of China's new energy industry through collaborative innovation network resilience

Prestige in social dilemmas: A network analytic approach to cooperation among Bogotá’s art organizations

Are intelligent peers liked more? Assessing peer-reported liking through the network analysis

Topological analysis, endogenous mechanisms, and supply risk propagation in the polycrystalline silicon trade dependency network