Complex Network Analysis Research Articles

A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection

There are many evolving dynamic networks in the real world, and community detection in dynamic networks is crucial in many complex network analysis applications. In this paper, a consensus community-based discrete spider wasp optimization (SWO) approach is proposed for the dynamic network community detection problem. First, the coding, initialization, and updating strategies of the spider wasp optimization algorithm are discretized to adapt to the community detection problem. Second, the concept of intra-population and inter-population consensus community is proposed. Consensus community is the knowledge formed by the swarm summarizing the current state as well as the past history. By maintaining certain inter-population consensus community during the evolutionary process, the population in the current time window can evolve in a similar direction to those in the previous time step. Experimental results on many artificial and real dynamic networks show that the proposed method produces more accurate and robust results than current methods.

Pinglu Canal Construction and Its Impact on Wildlife Habitat Connectivity: A Multi-Species Network Analysis and Ecological Restoration Strategy

The construction of the Pinglu Canal has significantly promoted economic development in southwestern Guangxi but it has also posed serious threats to wildlife habitat connectivity in the region. During the construction process, changes in land use types have led to habitat fragmentation, triggering a series of negative ecological effects, such as the “ecological island effect” and “edge effect”. This study uses the MaxEnt model to identify habitats for 11 wildlife species within the study area, categorized into terrestrial mammals, amphibians and reptiles, forest birds, and water birds, thereby constructing a multi-species habitat connectivity network (MHCN). Through complex network analysis, we assess habitat connectivity before and after the canal’s construction, as well as after implementing habitat optimization strategies. The results indicate that, following the completion of the Pinglu Canal, the wildlife habitat area decreased by 516.47 square kilometers, accounting for 5.79% of the total area, and the number of wildlife migration corridors decreased from 279 to 223, with a total decline of 56. Moreover, the average degree and clustering coefficient of habitat connectivity networks for various species showed a downward trend, while the average path length increased, indicating intensified habitat fragmentation and a growing threat to biodiversity in the region. In response, an ecological restoration strategy is proposed, which involves creating new habitats at key ecological “pinch points” and “barrier points,” increasing habitat area by 28.13% and the number of migration corridors by 33.41%, significantly enhancing the network’s robustness and connectivity. This research provides important references for biodiversity conservation and ecological restoration in the region.

Acupoint selection patterns for epilepsy in ancient texts based on visual network analysis

To analyze the disease patterns and acupoint selection characteristics of acupuncture for epilepsy in ancient acupuncture texts, providing references and ideas for clinical acupuncture treatment of epilepsy. Texts from the Chinese Medical Classics (5th edition) regarding acupuncture for epilepsy are reviewed. The frequency of acupoints, meridian association, distribution, specific points, corresponding epilepsy subtypes, and needling techniques are statistically analyzed. The Apriori algorithm is used for association rule analysis, and a complex network analysis is conducted for high-frequency acupoints and their corresponding subtypes and treatments. A total of 205 acupuncture prescriptions are identified. Ancient texts favored differentiation-based treatments for epilepsy, primarily classified into epilepsy, wind epilepsy, and five epilepsy. Commonly used acupoints include Baihui (GV20), Jiuwei (CV15), Shenmen (HT7), Shenting (GV24), and Xinshu (BL15), with a focus on the acupoints of the governor vessel, the bladder meridian, and the conception vessel. The acupoints on the head, face are combined with the acupoints on the limbs, with skillful use of the five-shu points and intersection acupoints. The most frequent combinations are Shenmen (HT7)-Baihui (GV20), Shenting (GV24)-Baihui (GV20), and Xinshu (BL15)-Shenmen (HT7). Visual network analysis revealed that Baihui (GV20)-Shenting (GV24), Baihui (GV20)-Shenmen (HT7), and Baihui (GV20)-Zhaohai (KI6) are core acupoint combinations. Treatment mainly involved moxibustion or combined acupuncture and moxibustion. The acupoint selection for epilepsy treatment in ancient texts is precise, frequently using Baihui (GV20), Jiuwei (CV15), Shenmen (HT7), Shenting (GV24), and Xinshu (BL15), etc., with emphasis on calming epilepsy, awakening the spirit, relaxing tendons, and nourishing the heart.

Evolutionary Game Analysis of Complex Networks in Enterprise Green Technology Innovation From a Prospect Theory Perspective

ABSTRACTGreen technological innovation in enterprises is a key driving force for achieving sustainable development and industrial transformation. However, how enterprises formulate effective innovation strategies by integrating subjective factors with objective market conditions within complex industrial networks remains an area requiring further exploration. This study aims to construct a complex network evolution model based on prospect theory and examines how subjective factors, such as reference points, risk preferences, and loss aversion, influence the adoption of green technology innovation in different network environments. It further explores how network characteristics, including topology, size, and node degree, affect the diffusion of innovation. Numerical analysis results indicate that in scale‐free networks, lowering reference points for enterprise gains, reducing loss aversion, increasing risk preference, expanding network size, and raising average node degree generally promote higher adoption of green technology innovation. In small‐world networks, the dependence on reference points is relatively lower, risk preference and average node degree demonstrate more complex impacts. Additionally, a moderate rewiring probability can enhance green technology innovation adoption in small‐world networks. These findings provide new insights and practical implications for understanding the driving mechanisms of green technological innovation in enterprises. They further emphasize the importance of government interventions tailored to the specific characteristics of industrial networks to effectively facilitate the diffusion of green technological innovation.

Bridging environmentally extended input-output models and complex network analysis: A bibliometric analysis of trends and opportunities

Bridging environmentally extended input-output models and complex network analysis: A bibliometric analysis of trends and opportunities

Network structures and mitigation potential of trade linked global agricultural greenhouse gas emissions

Since agriculture is a major source of greenhouse gas emissions, accurately calculating these emissions is essential for simultaneously addressing climate change and food security challenges. This paper explores the critical role of trade in transferring agricultural greenhouse gas (AGHG) emissions throughout global agricultural supply chains. We develop a detailed AGHG emission inventory with comprehensive coverage across a wide range of countries and emission sources at first. Utilizing this inventory, the multi-regional input-output analysis is integrated with the complex network analysis to specifically reveal the characteristics of global AGHG flow networks. Finally, scenario analyses reflecting current trends and policy changes in global agriculture are conducted to investigate the emission reduction potential of different networks. The results show that the community division and characteristics of different AGHG networks vary, with more communities in the rice-CH4 and livestock-CH4, N2O networks, and fewer in the cropland-N2O network. Production-side technology improvements (reducing direct carbon emission intensities) and consumption-side livestock products substitution can contribute to the reduction of global AGHG emissions. At the same time, these impacts may propagate to other countries through AGHG networks. In contrast, localization substitution has minimal impact on AGHG emissions and may even result in slight negative effects. It is suggested that enhancing cooperation between countries from a network perspective, such as strengthening the transfer of advanced production technologies within communities, could help reconceptualize global agricultural decarbonization.

Vertex Coloring and Eulerian and Hamiltonian Paths of Delaunay Graphs Associated with Sensor Networks

In this paper, we explore the connection between sensor networks and graph theory. Sensor networks represent distributed systems of interconnected devices that collect and transmit data, while graph theory provides a robust framework for modeling and analyzing complex networks. Specifically, we focus on vertex coloring, Eulerian paths, and Hamiltonian paths within the Delaunay graph associated with a sensor network. These concepts have critical applications in sensor networks, including connectivity analysis, efficient data collection, route optimization, task scheduling, and resource management. We derive theoretical results related to the chromatic number and the existence of Eulerian and Hamiltonian trails in the graph linked to the sensor network. Additionally, we complement this theoretical study with the implementation of several algorithmic procedures. A case study involving the monitoring of a sugarcane field, coupled with a computational analysis, demonstrates the performance and practical applicability of these algorithms in real-world scenarios.

Investigating Hypernode Classification of Complex Systems Based on High-order Graph Neural Networks

Investigating latent interactions beyond direct connections is essential for analyzing complex networks. However, traditional graph structures often fail to capture complex relationships, especially in the high-order interactions among multiple individuals. To address this issue, we extend the graph isomorphism network (GIN) framework to hypergraphs, treat nodes as self-hyperedges, and propose the self-hypergraph isomorphism network (SHGIN). Meanwhile, the hypergraph Weisfeiler–Lehman (WL) test is also proposed to distinguish different isomorphisms of hypergraphs and improve the representation power of hypergraph neural networks. Extensive experiments on co-authorship and co-citation networks demonstrate the effectiveness of SHGIN. The results indicate that our model displays superior hypernode classification accuracy compared to traditional graph neural networks in semi-supervised learning (SSL). Furthermore, it surpasses existing hypergraph neural network models in co-authorship datasets, highlighting its effectiveness in capturing high-order relationships in complex networks.

Study on the regularity of acupoint selection and compatibility of acupuncture and moxibustion in the treatment of Parkinson's disease based on complex network analysis

To analyze the regularity of acupoint selection and compatibility of acupuncture and moxibustion for Parkinson's disease (PD) using complex network analysis technology, so as to provide the basis and treatment strategies for clinical acupuncture and moxibustion treatment of PD. Relevant literature published in databases of China National Knowledge Infrastructure (CNKI), Wanfang, VIP, PubMed, and Web of Science from the inception to December of 2023 was retrieved. The literature was screened based on inclusion and exclusion criteria to establish a prescription database for acupuncture treatment of PD. Excel was used to analyze the frequency of use of acupoints, meridian normalization and specific acupoints. The IBM SPSS Modeler18.0 was used for modeling and association rule analysis, while the Gephi0.10.1 was utilized for complex network analysis. A total of 326 valid articles were included, from which 496 valid acupuncture prescriptions were extracted, involving 251 acupoints with a total usage frequency of 3, 968 times. The top 5 most frequently used acupoints were Baihui (GV20, 225 times), Taichong (LR3, 215 times), Fengchi (GB20, 202 times), Hegu (LI4, 192 times), and Sanyinjiao (SP6, 134 times). The second-order and third-order association rules between the acupoints indicated that the combinations of LI4-LR3 and GB20-LR3-LI4 had the highest support. After analyzing the complex network topology, 27 core acupoints for the treatment of PD were identified. Commonly used meridians are Governer Vessel, Gallbladder Meridian of Foot Shaoyang , Stomach Meridian of Foot Yangming. He-sea acupoints and yuan-source acupoints are the majority in the specific acupoints. The community analysis of the acupoints revealed 3 groups：the head acupoints group of the Governor Vessel, the acupoints group for nourishing the liver and kidney, and the acupoints group for expelling pathogens and purging excessiveness. Acupuncture and moxibustion treatment for PD often emphasizes the use of he-sea acupoints and yuan-source acupoints, and focus on combination of upper and lower acupoints, as well as the selection of acupoints within the same meridian. The treatment is based on zang-fu and meridian differentiation, with selection of the Governor Vessel to calm the mind and compatibility with acupoints to regulate the liver and tonify the kidney for the root causes, while utilizing limb acupoints to dispel wind, resolve phlegm, eliminate dampness, and purge heat for the symptoms.

Systematic Risks of the Global Lithium Supply Chain Network: From Static Topological Structures to Cascading Failure Dynamics.

Recent years have seen increasing concerns on supply chain risks of lithium, a critical material for achieving e-mobility transition and climate ambitions. These risks propagate both along the life cycle and across national boundaries in a multilayer network. However, most previous studies are either only based on static network measures or focused on individual layers, ignoring dynamic cascading risks and interconnected and interdependent relationships along life cycle stages and across economies. Here, we integrated trade-linked material flow and complex network analyses to investigate intricate interconnections, interdependencies, and systematic risks of the global lithium supply chain. Both static and dynamic measures of the global lithium supply chain network exhibit a "robust-yet-fragile" property: robust for random shocks yet fragile for targeted shocks and robust for small or local disruptions yet fragile for large or cascading failures. Portugal, Brazil, Singapore, Canada, Finland, Norway, South Africa, Israel, Hungary, and the United Arab Emirates are most likely to be affected by supply disruptions. A hypothetical USA-China trade decoupling will increase the severity and susceptibility of network-wide failures by around 5%. Our results call for global collaborations and collective efforts to balance efficiency and security and avoid a "zero-sum game" in securing the lithium supply chain.

The impact of country risk on nickel supply chain: based on complex network and panel regression analysis

Nickel is an important key resource and plays an increasingly important role in new energy technologies. The stability of its worldwide supply chain is crucial for addressing country risks and unforeseen events. To explore the impact of country risks on the stability of the global nickel supply chain, this study conducts a complex network analysis and panel regression analysis on nickel ore, ferro-nickel, and the stainless-steel trade data from 2000 to 2022. The study reveals the mechanisms by which economic and political risks affect the trade structure of nickel commodities and identifies the drivers of global nickel supply chain patterns. The study finds that an increase in economic risk promotes diversification of supply sources, while political risk hurts export scale and resource control ability. Industrial structure adjustments and R&D investments have a significant impact on trade structure, especially on the stainless-steel trade. China is a major consumer and importer, and occupies a central position in the global nickel trade network, while Indonesia and the Philippines’ policy changes have a significant impact on the market. This study provides an empirical basis for the risk management of the global nickel supply chain and a scientific basis for policy formulation and strategic planning.

Dynamic Evolution of the Power Structure and Contracting State Status in the Regional Trade Agreement Network and Its Trade Feedback Effects

ABSTRACT Against the backdrop of the serious setback in WTO multilateral negotiations, countries around the world have signed a large number of regional trade agreements (RTAs) to further reduce trade barriers and promote trade liberalization. These RTAs have formed a complex network of crisscrossing and mutually reinforcing dependencies that reshape the trade effects of individual RTAs. This paper uses the complex network analysis method to discuss the construction and development issues of RTAs among countries around the world and reveals the historical changes in the power structure of the economic and trade rules network and the dynamic evolution of the network status of core countries in international trade since 1995. Based on this, the feedback mechanism of the overall structure of the network and the node country status on the trade effect of RTAs is explored and tested. The analysis results show the following: (1) The global RTA network follows a power law distribution, the unbalanced characteristics of bipolarization in international economic and trade rules are very significant, and the gap in national network status is enormous. European countries are almost always in the center of the network, and China’s network status has not improved significantly. (2) The network shows obvious competitive and balancing relationships, and the pattern of international economic and trade rules has a trend toward short-term multi-polarization. (3) The test results of the trade effect feedback mechanism of the network show that the redundancy relationship of the overall network and the concentration of power structure have a negative influence on the trade effect of RTAs, while the bridging cluster function and transitivity of the overall network have a positive feedback effect on the trade effect of RTAs. The core position and control of a single country in the network form a positive feedback and high return on the trade effect of RTAs.

Study on the pattern and driving factors of water scarcity risk transfer networks in China from the perspective of transfer value—Based on complex network methods

The transfer value of risk, measured by the asymmetric impact of virtual water flows, is vital for regions addressing the global water crisis, achieving Sustainable Development Goal 6 (SDG6), and implementing sustainable water management. This study uses multi-regional input-output models, complex network analysis, and the Exponential Random Graph Model (ERGM) to construct and analyze China's water scarcity risk transfer network. Findings show frequent but weakly connected risk transfers between provinces, with significant spatial clustering. The network displays both random and hierarchical features, with clustered development being prominent. Reciprocal cooperation and extensive connections are crucial to avoiding the “prisoner's dilemma” in water conservation. High levels of “dual circulation” and water resource utilization enhance risk transfer value, while lower industrialization and digitization levels facilitate value reception. The efficiency of water resource use is key in shaping high-risk value pathways, underscoring the need for risk management at both source and sink ends. This study offers a scientific basis for improving water resource management, ensuring national water security, and redefining risk flow patterns.

Complex network analysis of fossil fuel functional regions in the United States during the period 2017 to 2022

In this paper we use complex network analysis to describe fossil fuel spatial flows among 132 places covering the entire United States in 2017 and in 2022. These spatial flows are for crude petroleum, gasoline, and oil fuels. The analysis shows that all three fuels have different network topology. For all six networks we find major hubs of crude petroleum and its products, gasoline and fuel oils, concentrated in areas with large reserves such as the south-central part of the US. Using modularity, a network cluster identification metric, we show that spatial interactions can be used to delineate functional regions and their differences across fuel types. These functional regions evolve over time in response to the shifting US role as a major producer and net exporter of fossil fuels, expansion of the domestic pipeline network, and increases in fuel production and refinement locations. The modal split of the fuels examined in this paper shows the dominant role pipelines play for crude petroleum, transporting approximately 83 % of tonnage in 2017 and increasing to almost 89 % in 2022. In contrast, gasoline and oil fuels modal split hovers at around 60 % of tonnage transported by tanker truck followed by other modes including pipelines. Our analysis shows geographic clustering of major hubs and their functional regions along the Gulf Coast in Texas and Louisiana. These are in places that are often the locations of natural disasters. This together with the rapid increase of a few hubs as gateways to fossil fuel US exports makes them prime candidates in disrupting fossil fuel supply chains worldwide and amplifies vulnerability of fossil fuel supply chains. The spatial clustering trends shown in this paper provide added evidence of the source of short-term negative impacts in places such as Chicago in Illinois and Corpus Christi in Texas. This offers added information for government intervention.

Gaussian process digital twin for voltage stability analysis of complex power networks under perturbation

Gaussian process digital twin for voltage stability analysis of complex power networks under perturbation

Complex network analysis of transmission networks preparing for the energy transition: application to the current French power grid

The worldwide process of replacing fossil fuels with low-carbon energy sources is underway. Existing energy networks are expected to be deeply modified in nature and structure during this transition. This work uses graph-theoretical statistical physics tools to analyze topology and structural changes of power grids, with the French grid as a case study. We discuss the small-world model to define an optimality criterion, the construction of a graph model for the French high-voltage transmission grid, and the development of a growth model to study the dynamics of such networks. The main result of our project suggests that the high efficiency level in the current French network is due to a high-voltage mesh interconnecting thermal power plants. Since implementing low-power-density renewable energy sources would imply non-trivial adjustments to maintain features, such as efficiency and robustness, these considerations must be added to economic and energetic assessments of transition scenarios.Graphic

Key Node Identification Method Based on Multilayer Neighbor Node Gravity and Information Entropy.

In the field of complex network analysis, accurately identifying key nodes is crucial for understanding and controlling information propagation. Although several local centrality methods have been proposed, their accuracy may be compromised if interactions between nodes and their neighbors are not fully considered. To address this issue, this paper proposes a key node identification method based on multilayer neighbor node gravity and information entropy (MNNGE). The method works as follows: First, the relative gravity of the nodes is calculated based on their weights. Second, the direct gravity of the nodes is calculated by considering the attributes of neighboring nodes, thus capturing interactions within local triangular structures. Finally, the centrality of the nodes is obtained by aggregating the relative and direct gravity of multilayer neighbor nodes using information entropy. To validate the effectiveness of the MNNGE method, we conducted experiments on various real-world network datasets, using evaluation metrics such as the susceptible-infected-recovered (SIR) model, Kendall τ correlation coefficient, Jaccard similarity coefficient, monotonicity, and complementary cumulative distribution function. Our results demonstrate that MNNGE can identify key nodes more accurately than other methods, without requiring parameter settings, and is suitable for large-scale complex networks.

Optimization of Growth Path for Enterprises Based on Complex Network Analysis and Embedded Neural Network Integration

Optimization of Growth Path for Enterprises Based on Complex Network Analysis and Embedded Neural Network Integration

The correlation evolution and formation mechanism of energy ecological efficiency in China: A spatial network approach

Enhancing energy ecological efficiency is critical for addressing climate change and advancing green, low-carbon economic development. This study utilizes an innovative Super Epsilon-based Measure (EBM) model alongside a gravity model to evaluate the energy ecological efficiency across 30 provincial-level regions in China from 2011 to 2021. This research fills a critical gap in the literature by combining efficiency assessments with complex network analysis, offering a novel perspective on the spatiotemporal evolution and distribution of energy efficiency. The findings reveal that: (1) China's energy ecological efficiency exhibited a fluctuating growth trend over the study period, with significant regional disparities characterized by the pattern “eastern region > central region > western region.” (2) Overall network density, network correlation, and the number of network relationships maintaining relatively stable growth, with a growth rate of 28.93 %, 27.51 % and 28.89 %, respectively. However, the efficiency network's overall connectivity remains low, with fewer correlation lines in the western and central regions and higher network density in the eastern coastal areas. (3) Quantitative analysis using the Quadratic Assignment Procedure (QAP) method identifies key factors influencing network formation, such as urban openness, industrialization level, environmental regulation, economic development, geographical proximity, population quality, and resource endowment. Notably, every 1 % increase in the industrial agglomeration level will have a negative impact of −0.248 % on the energy ecological efficiency network. This research fills a gap in understanding the structural and dynamic aspects of China's energy ecological efficiency, offering novel insights that can guide policymakers in formulating strategies to enhance sustainable development.

Seismic shocks and financial systems: a topological perspective on Borsa Istanbul after the earthquake

This study examines the impact of the Kahramanmaraş earthquake on the BIST100 index through the application of complex network analysis. The method begins by examining the correlation distance between the logarithmic returns of companies in the BIST100 index, which serves as the foundation for creating filtered weighted networks. This technique prioritizes robust financial connections while downplaying weaker ones, enabling a thorough examination of the network topological structure and identifying noteworthy financial interactions. The study employs a combination of global and local topological metrics, as well as structural entropy, to gain insights on the interconnections among markets in terms of public interest and the roles played by various entities. It demonstrates how exogenous shocks impact the network structure and reaction. Findings indicate initial notable alterations in the network framework, which were subsequently alleviated by the implementation of financial rules and market processes. This demonstrates the ability of financial markets to recover and maintain stability following a disaster. Results offer useful perspectives on the interplay between market dynamics and the resilience of the financial system in the presence of natural disasters.