Data-Driven Dynamic Network Modeling for Analyzing the Evolution of Product Competitions

Advances in exponential random graph ( p*) models applied to a large social network

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.

This paper introduces a novel approach for identifying dynamic triadic transformation processes, applied to five networks: three undirected and two directed. Our method significantly enhances the prediction accuracy of network ties. While balance theory offers insights into evolving patterns of triadic structures, its effects on overall network dynamics remain underexplored. Existing research often neglects the interaction between micro-level balancing mechanisms and overall network behavior. To bridge this gap, we develop a method for detecting dynamic triadic structures in signed networks, categorizing triangle transformations over two consecutive periods into formation and breakage. We analyze the impact of these structures on temporal network evolution by incorporating them into exponential random graph models across five networks of varying size, density, and directionality. To address the complexity of multi-layer networks derived from signed networks, we modify the temporal exponential random graph model framework. Our method significantly improves out-of-sample prediction accuracy for network ties, with additional predictive power from incorporating negative network information. These findings highlight the importance of considering the triadic transformation processes of balance triangles in studying temporal networks, validated across diverse datasets, warranting further research.

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

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.

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.

Understanding the structure and determinants of intercity carbon emissions association network in China

The exponential random graph model (ERGM) is an increasingly popular method for the statistical analysis of networks that can be used to flexibly analyze the processes by which policy actors organize into a network. Often times, interpretation of ERGM results is conducted at the network level, such that effects are related to overall frequencies of network structures (e.g., the number of closed triangles in a network). This limits the utility of the ERGM because there is often interest, particularly in political and policy sciences, in network dynamics at the actor or relationship levels. Micro‐level interpretation of the ERGM has been employed in varied applications in sociology and statistics. We present a comprehensive framework for interpretation of the ERGM at all levels of analysis, which casts network formation as block‐wise updating of a network. These blocks can represent, for example, each potential link, each dyad, the out‐ or in‐going ties of each actor, or the entire network. We contrast this interpretive framework with the stochastic actor‐based model (SABM) of network dynamics. We present the theoretical differences between the ERGM and the SABM and introduce an approach to comparing the models when theory is not sufficiently strong to make the selection a priori. The alternative models we discuss and the interpretation methods we propose are illustrated on previously published data on estuary policy and governance networks.

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.

Synthetic power grids enable real-world energy system simulations and are crucial for algorithm testing, resilience assessment, and policy formulation. We propose a novel method for the generation of synthetic transmission power grids using exponential random graph (ERG) models. Our two main contributions are (1) the formulation of an ERG model tailored specifically for capturing the topological nuances of power grids and (2) a general procedure for estimating the parameters of such a model conditioned on working with connected graphs. From a modeling perspective, we identify edge counts per bus type and k-triangles as crucial topological characteristics for synthetic power-grid generation. From a technical perspective, we develop a rigorous methodology to estimate the parameters of an ERG constrained to the space of connected graphs. The proposed model is flexible, easy to implement, and successfully captures the desired topological properties of power grids. Published by the American Physical Society 2024

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.

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

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.

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