Complex Network Perspective Research Articles

Study on the influence diffusion of SMEs in open-source communities from the perspective of complex networks.

In the era of digital economy, enterprise research and development (R&D) tends to be open-source. Due to their limited resources, small and medium-sized enterprises (SMEs) can join open-source platforms to get additional creative resources and technical support. In this context, from the perspective of complex networks, the influence diffusion of SMEs after embedding open-source innovation networks is studied in this paper. First, an integrated simulation model including a network model, agent model and innovative diffusion model is constructed. Second, the influence diffusion strategy is proposed considering initial impact, embedding timing and connection mode (same-match and heterogeneous) of the enterprise. Third, the dynamic simulation of the influence diffusion process of SMEs demonstrates that embedding timing has a significant impact. There is no significant difference in the influence diffusion at the early and mature stages in the evolution process of open-source innovation networks. The initial impact of enterprises has a significant influence on the diffusion during the developing period, but the effect on its influence diffusion at the initial and mature stages is not obvious. Finally, in light of experiment results, it is clear that the open-source platform plays an important role on the growth of SMEs as evidenced by the close correlation between the spread of SMEs' influence within the open-source innovation network and the community's stage of development.

Manufacturing service network characteristics analysis of digital twin system under edge computing and cloud edge collaborative computing environment

In order to optimise the configuration of manufacturing services in the virtual and real space and improve the efficiency of the collaboration of manufacturing service resources, we investigate the digital twin systems that provide various services under different service computing modes, analyse their basic network structure from the perspective of complex networks, and reveal internal interaction methods and feedback control process to optimise virtual and real resource allocation and improve physical system stability. On this basis, the characteristics of the manufacturing service network in the digital twin system are further analysed and explained, and the mechanism of manufacturing service collaboration in the virtual-real interaction fusion mode is described.

Prospects of Omni-Channel Marketing from the Perspective of Complex Networks: A CiteSpace Knowledge Graph Analysis

Prospects of Omni-Channel Marketing from the Perspective of Complex Networks: A CiteSpace Knowledge Graph Analysis

The Evolution and Knowledge Change of Innovation Cooperation Network in Platform Ecosystem: A Computer Simulation From Complex Network Perspective

Platform ecosystems, as a new organizational form, provide enterprises with new contexts for innovation and entrepreneurship. However, due to a lack of dynamic data, we do not understand how the platform ecosystem evolves. Because the traditional methods have limitations, this study uses computer simulations to investigate the evolution and knowledge change of innovative cooperation networks in the platform ecosystem from the perspective of complex networks. The results indicate that the platform ecosystem’s innovation cooperation network evolves around initial network enterprises. The degree of enterprise cooperation shows a trend of decreasing first then increasing, but the efficiency of enterprise cooperation gradually decreases. Second, as the network evolves, the knowledge level rises, whereas the knowledge growth rate falls in the platform ecosystem. Meanwhile, the impact of network structure on knowledge change is unclear, whereas enterprise knowledge creation capability, knowledge absorption capability, and competitive pressure all have a significant impact on knowledge change.

Patterns of embodied pollutant emissions along the global light industrial chain: A complex network perspective

With the upgrading of consumer goods and accelerating trade globalization, enormous amounts of pollution related to the light industry sector's production are transferred across borders along the global industrial chain, but their network patterns and evolution remain unclear. Here, we developed an analytical framework for trade-embodied pollutant emissions transfer (TPET) network by combining complex network theory and input– output theory to analyze the network of 16 light industries in 43 economies from 2000 to 2014. We revealed the critical economies, key industries, critical paths and their evolution trends. The light industry sector's total TPET was consistently large, but many factors (e.g., upgrading consumer goods, increasing trade volume, the 2008 financial crisis) caused large fluctuations. The network densities remained relatively high, with an obvious small-world property and significant centralization. Large economies (especially the world's top 10 economies with a total GDP exceeding US $1.86 trillion in 2014) were critical nodes due to their stable and dominant importance, but played opposite roles (i.e., net TPET importers vs. net exporters). Due to large differences in domestic demand, and environmental regulations, high-income economies (e.g., the U.S., and Germany) were the leading net TPET exporters, whereas low- or middle-income economies (e.g., China, Russia) were the dominate net importers; TPET therefore primarily flowed from high-income to low- or middle-income economies, with substantial growth over time. The electricity, gas, steam and air conditioning supply industry, the manufacture of other non-metallic mineral products, the manufacture of basic metals, and the fishing and aquaculture industry together accounted for 81.2 and 89.0% of total trade-embodied NOX and SOX emissions, respectively; they were thus key industries. To reduce emissions, critical economies should consume more environment-friendly products, strengthen technological innovation and pollution control in key industries, and expand environmental cooperation with other countries. Our results provide insights into the global light industry sector's TPET characteristics and reduction strategies. They also provide a reference for adjusting trade patterns and industrial structures.

Exploring the topological characteristics of urban trip networks based on taxi trajectory data

As an essential mode of travel for city residents, taxis play a significant role in meeting travel demands in an urban city. Understanding the modal characteristics of taxis is vital to addressing many difficulties regarding urban sustainability. The movement trajectory of taxis reflects not only the operating features of taxis themselves but also urban structure and human mobility. In this work, the taxi trajectory data of Chengdu and New York City is processed, and the corresponding urban trip networks are constructed based on geographic information systems. We empirically and systematically analyze these urban trip networks according to the network hierarchy based on complex network theory. First, we studied the low-order organization of the urban trip networks (i.e., degree distribution, cluster-degree coefficient, rich-club coefficient, and so on.). We uncover the nontrivial relationship between network density and trip distance and find that the urban trip network in Chengdu is more heterogeneous than that in New York City. Second, we investigate the meso-order organization of the urban trip networks by using community detection. The community detection results show that the community boundaries are more or less mismatched with the administrative boundaries. Finally, we detect the higher-order organizations of the urban trip networks and find some critical nodes and regions. These empirical results from the perspective of complex networks provide insight to better understand the urban structure and human mobility, and potentially amend urban planning.

How urban metro networks grow: From a complex network perspective

Metro lines emerged and developed in cities driven by various factors, and resulted in different structures as we see today. How do metro lines grow as a system? The spatiotemporal growing patterns of metro networks and their complex-network statistics may shed light on this question. Within the framework of the complex network analysis, this paper establishes a nine-metric scheme to quantify the service performance of an urban metro network from three dimensionalities i.e., accessibility, resilience, and serviceability. Accordingly, a shamrock plot is proposed as a tool to visualize the multi-dimensional maturity of a metro network as well as its growing pattern. The metrics and the visualization tool are used to reveal metro development in 42 cities of Chinese mainland. The results show that metro networks can evolve from nascent, to skeleton, and to mature forms, with their scores increasing in all dimensionalities. The choice of urban transportation between metro-dominant and automobile-dominant results from the race between metro development and car popularity. Cities may be more likely to become metro-dominant if metro develops early at a low level of car ownership. Urban metro network may develop as a response to the city’s needs hierarchically from the most basic (e.g., accessibility) to more advanced (e.g., resilience and serviceability), similar to Maslow’s hierarchy of individual needs.

Robustness test of multiple protection strategies for ecological networks from the perspective of complex networks: Evidence from Wuhan Metropolitan Area, China

AbstractRobustness is the key for strategies to protect ecological network (ENs) against external threats. In this study, we proposed a research framework for robustness testing of EN protection strategies. First, we constructed the ENs of Wuhan Metropolitan Area (WHM) based on the advantages of circuit theory in landscape ecology. We then extended complex network theory to the migration application of ENs. Five protection strategies of ENs were antagonistically combined with multiple attack modes to implement multi‐scenario simulations of attacks on ENs. The robustness parameters of the complex network were applied to measure the effectiveness of EN protection strategies under different attack efficiencies. The results showed that identifying the key performance of ENs makes hierarchical protection of ENs possible. Therefore, when focusing on maintaining the transmission efficiency and global connectivity, or preventing fragmentation of ENs in WHM, we recommend protecting the main trunks of ENs. When focusing on maintaining the backbone connectivity of ENs in WHM, we recommend protecting the main‐secondary trunks of ENs. We argue that specific protection strategies and directions can be determined according to the objectives of ecological construction and the scheme we put forward.

A 12-lead ECG correlation network model exploring the inter-lead relationships

The 12-lead electrocardiogram (ECG) is widely used for automatic diagnosis of arrhythmia based on deep neural networks (DNN). In this paper, we use the 12-lead ECG dataset provided by the China Physiological Signal Challenge 2018 (CPSC2018), which contains 6877 samples and each sample contains 12-lead ECG records and corresponding reference labels. First, by statistical analysis of the results of 90 DNN models with F 1 > 0.6 published by CPSC2018, we found that almost all DNN models had high accuracy in identifying the left bundle branch block (LBBB) even when the number of training samples for LBBB is severely insufficient. Second, through ablation studies, we found that the absence of the 7th lead V1 severely affected the diagnostic accuracy of many DNN models, where ablation studies were used to quantify the effect of the disappearance of a single lead on the F 1 of the DNN model. We aim to explain the above two special phenomena using complex network theory. A 12-lead ECG correlation network based on the inter-lead Pearson correlation coefficient is proposed, which allows us to observe the correlation between a single lead and others, and quantify the correlation strength of each lead through a projection process. We used the covariance method to quantify the consistency of the change trend for the average correlation strength of 12 leads between any two categories, and found that the mean values of the covariance for LBBB under the positive and negative 12-lead ECG correlation network were 0.01 and 0.07, respectively, much smaller than other categories. This uniqueness may explain from the perspective of complex networks why LBBB can be diagnosed accurately by almost all DNN models when its number of samples used for training is severely insufficient. Furthermore, we found that the correlation between the lead V1 and other leads was close to 0. This low correlation may make the information of the lead V1 significantly different from other leads, resulting in its important role in the automatic diagnosis of arrhythmia.

Evolution Features and Robustness of Global Photovoltaic Trade Network

Photovoltaic industry trade has become a global trade activity, and a wide range of photovoltaic trade relations have been formed between countries. In order to further strengthen and balance trade relations, this paper analyzes global photovoltaic (PV) trade from the perspective of complex networks. We employ network indicators and the cascading process of risk propagation to analyze the evolution features and the vulnerability of the PV trade network. Firstly, we establish the global PV trade networks from 2000 to 2021 based on the PV trade flow between countries. We then explore evolution features and analyze the influencing factors of the trade network structure. Finally, we simulate the cascading process of risk propagation on the trade network based on an improved bootstrap percolation model. The evolution features reveal the following three results: (1) the scale of global PV trade continues to grow, and the participation of some countries has increased significantly; (2) the global PV trade network has small-world characteristics, and the related products have high circulation efficiency; and (3) the global PV trade network has a core-periphery structure, while a few countries drive most of the trade. China, Germany, and the U.S. are the top PV traders. Some Asian countries, such as Vietnam, are gradually increasing their share of the market. The QAP regression analysis shows that the gaps in GDP and electricity access rate are the biggest facilitating and hindering factors in the global PV trade, respectively. The simulation results show that the global PV trade network is vulnerable to targeted risk and is robust to randomness risk.

Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective

Artificial intelligence (AI), as a rapidly developing interdisciplinary field, is a key driver of future economic development. The Yangtze River Delta (YRD) is one of the most significant economic regions of China, which also has a leading role in the AI industry. In this study, based on the patent cooperation data of YRD in the past decade, we focus on studying the collaborative innovation relationship in the AI field of the YRD from the perspective of complex networks. In order to investigate the interprovincial, intra-city and inter-city collaborative innovation relationships, we construct the Yangtze River Delta AI collaborative innovation (YRD-AICI) network. Subsequently, to analyze the development status and collaborative innovation relationship of innovation bodies in the AI field of YRD, we construct the Yangtze River Delta AI patent cooperation (YRD-AIPC) network. Next, the basic characteristics and spatio-temporal evolution of these two networks are explored, and the research results are presented that: (1) Shanghai, Jiangsu Province, and Zhejiang Province have obvious leading advantages in the AI field of the YRD, and the development gap between cities is significant; (2) the pioneering innovation bodies in the AI industry of the YRD are identified using centrality measures, and their cooperative innovation relationship is revealed; (3) based on link prediction methods, future partnerships between cities and innovation bodies are predicted to provide the future development trend of the YRD. The results provide theoretical support for exploring the cooperation mechanism of collaborative innovation in the AI field of YRD and inspire future development planning.

Dynamic analysis and community recognition of stock price based on a complex network perspective

In the context of the 2008 financial crisis, this paper focuses on the Chinese A-share stock prices in ten years (from January 1, 2003 to December 31, 2012) to explore some possible cautionary phenomena based on a complex network perspective. Firstly, with the help of the threshold method and taking monthly and fixed time (20 days, 40 days and 60 days) as time windows, this paper establishes highly correlated networks of daily opening price and backward answer authority closing price respectively. Notice that the correlation between stock price and its category in Citic One-level industry index is misty. And then, it is interesting to discover some special behaviors of stock prices by the evolution of different network features and community identification, so as to provide guidance for crisis identification and control. The results show that the size of time windows has a certain influence on the evolution, and network parameters appear obvious particularity near the 2008 crisis for the networks with fixed times of monthly and 20 days. For example, the average path length reaches a maximum around May 2009; the average degree is low and stable around May 2005 to December 2006 but fluctuates greatly around May 2008 to September 2008; the clustering coefficient fluctuates greatly and reaches the maximum value 0.78 during May 2008 to August 2008; the assortativity coefficient reaches its maximum value at a certain time and fluctuates relatively greatly. In addition, the community identification results indicate that near the crisis, cluster communities with high network density, which contain a relatively large number of nodes, appear.

A novel method of heterogeneous combat network disintegration based on deep reinforcement learning

Modern war is highly dependent on intelligent, unmanned combat systems. Since many intelligent, unmanned combat systems have network attributes, it is meaningful to research combat systems from the perspective of complex network. Heterogeneous network provides a suitable model to describe real combat network. Previous studies of combat network only concentrate on homogeneous networks. However, on the real battlefield, military networks are composed of a large number of heterogeneous nodes and edges with different functions. In the paper, a superior, intelligent, heterogeneous combat network disintegration strategy (HDGED) are obtained by DQN, which embeds heterogeneous networks into a low-dimensional representation vector as input, rather than ignore the differences of the nodes and their connections. A method of heterogeneous graph embedding is first introduced, which adopts type encoding and aggregation. Besides, a normalized combat capability index was designed, which could assess the performance of the dynamic heterogeneous combat networks. On this basis, HDGED was experimented on networks with uneven node combat capabilities and the results show that HDGED has improved disintegration effectiveness for heterogeneous networks of different sizes compared with traditional methods. Our work provides a new approach to realize the disintegration of heterogeneous combat networks by deep reinforcement learning, which is of great significance for optimizing the command operation process, and deserves further study.

The Convergence Evolution in Europe from a Complex Networks Perspective

The evolution of the convergence among the European countries, including both Eurozone as well as non-Eurozone economies, is investigated in this paper. To do so, we construct correlation-based networks and study them by employing the Threshold Weighted-Minimum Dominating Set (TW–MDS) algorithm and analyzing standard quantitative performance graph theory metrics. Each country is represented by a network node, while the edges represent the cross-correlations calculated for a specific macroeconomic variable, for a given time window. To study the intertemporal evolution of the network’s interconnections, we examine its structure in three consecutive time intervals: 1999–2004, 2005–2010 and 2011–2019. The empirical findings provide a mixed pattern. The European countries exhibit a common behavior over time for some macroeconomic variables, but not for all of them.

Investigating internal migration with network analysis and latent space representations: an application to Turkey.

Human migration patterns influence the redistribution of population characteristics over the geography and since such distributions are closely related to social and economic outcomes, investigating the structure and dynamics of internal migration plays a crucial role in understanding and designing policies for such systems. We provide an in-depth investigation into the structure and dynamics of the internal migration in Turkey from 2008 to 2020. We identify a set of classical migration laws and examine them via various methods for signed network analysis, ego network analysis, representation learning, temporal stability analysis, community detection, and network visualization. The findings show that, in line with the classical migration laws, most migration links are geographically bounded with several exceptions involving cities with large economic activity, major migration flows are countered with migration flows in the opposite direction, there are well-defined migration routes, and the migration system is generally stable over the investigated period. Apart from these general results, we also provide unique and specific insights into Turkey. Overall, the novel toolset we employ for the first time in the literature allows the investigation of selected migration laws from a complex networks perspective and sheds light on future migration research on different geographies.

Research of Software Defect Prediction Model Based on Complex Network and Graph Neural Network

The goal of software defect prediction is to make predictions by mining the historical data using models. Current software defect prediction models mainly focus on the code features of software modules. However, they ignore the connection between software modules. This paper proposed a software defect prediction framework based on graph neural network from a complex network perspective. Firstly, we consider the software as a graph, where nodes represent the classes, and edges represent the dependencies between the classes. Then, we divide the graph into multiple subgraphs using the community detection algorithm. Thirdly, the representation vectors of the nodes are learned through the improved graph neural network model. Lastly, we use the representation vector of node to classify the software defects. The proposed model is tested on the PROMISE dataset, using two graph convolution methods, based on the spectral domain and spatial domain in the graph neural network. The investigation indicated that both convolution methods showed an improvement in various metrics, such as accuracy, F-measure, and MCC (Matthews correlation coefficient) by 86.6%, 85.8%, and 73.5%, and 87.5%, 85.9%, and 75.5%, respectively. The average improvement of various metrics was noted as 9.0%, 10.5%, and 17.5%, and 6.3%, 7.0%, and 12.1%, respectively, compared with the benchmark models.

The study of new energy vehicle choice in China from the perspective of complex neural network

China has become the world’s largest market for the production and sales of new energy vehicles. In the Internet era, online review data is becoming more and more important, and it is a great challenge for new energy vehicle manufacturers and consumers to quickly obtain and find out the effective information in the review data. In view of the above understanding, this study uses the Bert-wwm-ext model structure, data mining, and deep learning to study the new energy vehicle selection, and also analyzes the positioning of domestic and foreign new energy vehicle brands and their brand development from the perspective of complex networks. The research results found that: 1) Consumers pay more and more attention to the quality of new energy vehicles. 2) The comparative analysis of BEV and PHEV reveals that consumers’ evaluation of both types of vehicles is roughly comparable, but the difference in satisfaction with the endurance of both types of vehicles is very obvious. 3) Most of the brands of new energy vehicles are concentrated in the price range of RMB80,000 to RMB350,000, and within this range, consumers’ evaluation is positively correlated with the price of the vehicle. Among the new energy vehicle brands over RMB350,000, consumer evaluation does not rise with the price of the vehicle. 4) The head effect of Chinese brands is significant, Foreign brands have formed strong brands with high brand premiums.

CRA: Identifying Key Classes Using Markov-Chain-Based Ranking Aggregation

Researchers have proposed many approaches to identify key classes in software from the perspective of complex networks, such as CONN-TOTAL-W, PageRankBR, and ElementRank, which can effectively help developers understand software. However, these approaches tend to rely on a single metric when measuring the importance of classes. They do not consider the aggregation of multiple metrics to select the winner classes that rank high in majority metrics. In this work, we propose a key class identification approach using Markov-Chain-based ranking aggregation, namely CRA. First, CRA constructs a weighted directed class coupling network (WDCCNet) to describe the software and further applies existing approaches on WDCCNet to calculate class importance. Second, CRA filters out some metrics according to specific rules and uses the Markov chain to aggregate the remaining metrics. When the state probability distribution reaches a fixed point and does not change anymore, the classes in the software are sorted in a descending order according to the probability distribution, and the top-15% classes are treated as key classes. To evaluate the CRA approach, we compare it with 10 baseline approaches available on 6 pieces of software. Empirical results show that our approach is superior to the baselines according to the average ranking of the Friedman Test.

The trend of disruption in the functional brain network topology of Alzheimer’s disease

Alzheimer’s disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain’s functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer’s disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

Resilience of supply-chain systems under perturbations: A network approach.

Supply-chain systems (SCSs) are an indispensable part of our daily infrastructures. Note that a small perturbation in a SCS can be amplified, eliciting cascading failures. It is of significant value to ensure a high resilience of SCSs. However, due to the complexity of SCSs, it is quite challenging to study their resilience under conditions of perturbations. In view of this, this paper presents a complex network perspective toward the resilience of SCSs. To achieve this goal, a complex SCS is modeled as a multilayer supply-chain network (SCN) with physical organizations being modeled as nodes and interactions among them as edges. A modeled SCN contains three types of nodes, i.e., suppliers, manufacturers, and retailers. An algorithm is proposed to construct a multilayer SCN. For each layer of a multilayer SCN, two kinds of networks, i.e., networks with Poisson degree distributions and networks with power-law degree distributions, are considered. For a given multilayer SCN, a ripple-effect network model is proposed to analyze its resilience under perturbations. Regarding the perturbations, two scenarios, i.e., random node failures and target node failures, are adopted in this research. In order to validate the effectiveness of the proposed network perspective, simulations on computer-generated SCNs are carried out. Interestingly, it is found that the resilience of SCNs under both random and target perturbations presents a discontinuous phase-change phenomenon, which indicates that SCNs are quite fragile under perturbations. It is further noticed that SCNs with power-law degree distributions are relatively more robust than SCNs with Poisson degree distributions. Although SCNs are found to be fragile, it has been discovered that denser interactions between different system organizations can enhance the network's resilience.