Application Of Complex Network Theory Research Articles

Progress in complex network theory-based studies on the associations between health-related behaviors and chronic non-communicable diseases

In recent years, the research focus on health-related behavior and chronic non-communicable diseases has shifted from the analysis on independent effects of multiple causes on a single outcome to the evaluation the complex relationships between multiple causes and multiple effects. Complex network theory, an important branch of system science, considers the relationships among factors in a network and can reveal how health-related behaviors interact with chronic diseases through a series of complex network models and indicators. This paper summarizes the definition and development of complex network theory and its commonly used models, indicators, and case studies in the field of health-related behavior and chronic disease to promote the application of complex network theory in the field of health and provide reference and tools for future research of the relationship between health-related behavior and chronic disease.

Current Status and Perspective of Vulnerability Assessment of Cyber-Physical Power Systems Based on Complex Network Theory

The increasing factors of uncertainty faced by the system are due to the deep coupling of the electric power cyber network and the physical network. Consequently, ensuring the efficient, secure, and stable operation of the cyber–physical power system (CPPS) has become a key concern. To achieve this, vulnerability assessment plays a crucial role, as it identifies and protects the vulnerable points of the system. The application of complex network theory to assess the vulnerability of CPPSs has garnered significant attention from scholars. This paper delves into the research connotation of vulnerability assessment for CPPSs, starting with the origin, definition, and classification of vulnerability. Subsequently, the assessment framework of vulnerability based on complex network theory is presented, and the status of current domestic and international research in this field is summarized. Furthermore, the interrelationship between system vulnerability and cascading failures is analyzed from the perspective of complex network theory. In conclusion, the ideas of CPPS coupling modeling in vulnerability assessment are summarized, the concept of situation awareness is introduced, and a prospective approach for dynamic vulnerability assessment is proposed. This approach is based on situation awareness combined with complex network theory. Security protection and optimal operation of CPPSs based on vulnerability assessment are also discussed, along with the assessment of vulnerability within integrated energy cyber–physical systems (IECPSs).

The application of complex network theory for resilience improvement of knowledge-intensive supply chains

With frequent political conflicts and public health emergencies, global supply chains are constantly under risk interference, significantly reducing supply chain resilience (SCR), especially for the knowledge-intensive supply chains (KISCs). To assess and improve the resilience of KISC, this paper uses complex network theory to construct a directed weighted network model suitable for KISC and expresses the SCR as a comprehensive capability that can resist risk and recover from it. Using quantitative indicators plus qualitative assessment to quantify the resilience index and identify the network key nodes. Two resilience improvement paths are proposed for KISCs, improving firms’ development capacity and industrial backup. In the case study, the resilience of the integrated circuit (IC) supply chain is assessed and improved according to real data from the global IC industry. The findings show that (i) The resilience assessment based on the directed weighted network aligns with industrial reality. (ii) Improving firms’ development capability and industrial backup can improve SCR. (iii) Effective improvement of resilience requires targeting key nodes in the supply chain network (SCN). Moreover, the degree of firms’ development capability improvement and industrial backup intensity should be within a specific range.

A Maximum Degree related Condition to Asymmetric Game in Weighted Vertex Cover Networks

Minimum weighted vertex cover (MWVC) is an important optimization problem in the application of complex network theory, and the asymmetric game model sheds light on the essential mechanism of the MWVC. In this paper, a new condition for the effectiveness of the asymmetric game is discovered based on the imbalance of weighted networks. When the new condition satisfies, a Nash equilibrium (NE) becomes a bridge state between vertex cover (VC) state and MWVC state, and any initial state of the network can reach the NE following the feedback-based best response (FBR) algorithm. Numerical experiments verify that this new condition has stronger stability and wider range of applications than the original one. FBR algorithm with the new condition shows superiority over other distributed algorithms. We also qualitatively clarify the network property factors affecting the performance of MWVC to some extent.

Peertrap: An Unstructured P2P Botnet Detection Framework Based on SAW Community Discovery

Botnet has become one of the serious threats to the Internet ecosystem, and botnet detection is crucial for tracking and mitigating network threats on the Internet. In the evolution of emerging botnets, peer-to-peer (P2P) botnets are more dangerous and resistant because of their distributed characteristics. Among them, unstructured P2P botnets use custom protocols for communication, which can be integrated with legitimate P2P traffic. Moreover, their topological structure is more complex, and a complete topology cannot be obtained easily, making them more concealed and difficult to detect. The bot itself is a kind of overlay network, and research shows that the nodes with shared neighbors usually belong to a certain community. Aiming at unstructured P2P botnets and exploiting complex network theory, from the perspective of shared neighbor nodes, this article proposes a botnet detection framework called Peertrap based on self-avoiding random walks (SAW) community detection under the condition of incomplete topological information. Firstly, network traffic is converted into Netflow, by utilizing Apache Flink big data platform. Also, a P2P traffic cluster feature extraction rule is proposed for distinguishing P2P traffic from non-P2P traffic, and it is formulated by using the upstream and downstream traffic and address distribution threshold features. Then, the confidence between P2P clusters is calculated by the Jaccard coefficient to construct a shared neighbor graph, and the same type of P2P communities are mined by hierarchical clustering using SAW algorithm combined with PCA. Finally, two community attributes, mean address distribution degree and mean closeness degree, are used to distinguish botnets. Experiments are conducted on three unstructured P2P botnets datasets, Sality, Kelihos, and ZeroAccess, and the CTU classic datasets, and then good detection results can be achieved. The framework overcomes one of the most critical P2P botnet detection challenges. It can detect P2P bots with high accuracy in the presence of legitimate P2P traffic, incomplete information network topology, and C&C channel encryption. Our method embodies the typical application of complex network theory in botnet detection field, and it can detect botnets from different families in the network, with good parallelism and scalability.

Modeling Resilience of Air Traffic Management Systems Based on Complex Networks

The air transport market has a strategic character, promotes economic and social development, and has a strong correlation with the level of economic activity. The Air Traffic Management (ATM) system plays an essential role in air transportation and can be characterized as a sociotechnical system that is too complex to research through classical approaches such as systems engineering. In this sense, the application of Complex Network Theory (CNT) analysis and modeling paradigms is driven by the need to accommodate the growth of air traffic within an already saturated ATM infrastructure. The present work describes the development of a resilience evaluation model of ATM systems based on CNT, its metrics and analysis tools. The model was applied to the Brazilian ATM system, demonstrating its usefulness in identifying the air traffic control (ATC) agencies that have the greatest influence on the network. The results also showed that the Brazilian ATM network is resilient to random failures; however, it is particularly vulnerable to targeted attacks to the ATC agencies with the highest centrality. The research findings can be applied to prioritize the deployment of systems and equipment that enhance the resilience and operability of the Brazilian ATM system.

Application of complex network theory in identifying critical elements of CRH2 train system

Purpose Based on complex network theory, a method for critical elements identification of China Railway High-speed 2 (CRH2) train system is introduced in this paper. Design/methodology/approach First, two methods, reliability theory and complex theory, are introduced, and the advantages and disadvantages for their application in identifying critical elements of high-speed train system are summarized. Second, a multi-layer multi-granularity network model including virtual and actual nodes is proposed, and the corresponding fusion rules for the same nodes in different layers are given. Findings Finally, taking CRH2 train system as an example, the critical elements are identified by using complex network theory, which provides a reference for train operation and maintenance. Originality/value A method of identifying key elements of CRH2 train system based on integrated importance indices is introduced, which is a meaningful extension of the application of complex network theory to identify key components.

Complex scheduling network: an objective performance testing platform for evaluating vital nodes identification algorithms

With the widespread application of complex network theory, identifying vital nodes is an important part of complex network analysis, which has been a key issue in analyzing the characteristics of network structure and functions. Although many centrality measures have been proposed to identify vital nodes, there is still a lack of an objective performance testing platform for evaluating vital nodes identification algorithms. This study introduces the complex scheduling network as an objective performance testing platform where the identification of vital nodes directly determines the decision-making in each decision scenario through the development of heuristic algorithms upon centrality measures, and therefore, the optimization goals of scheduling problems can be regarded as an objective index for evaluating vital node identification algorithms. Finally, the undirected network obtained by the open shop scheduling problem is taken as an example to analyze and evaluate the performance of various vital node identification algorithms.

Stable generalist species anchor a dynamic pollination network

AbstractThe application of complex network theory to community ecology has enabled quantification of interactions among large suites of species and clarified patterns of community structure across systems. Past analyses, however, have assumed that ecological networks are temporally static and persistent and spatially homogeneous, which could confound inference if species interactions vary over time and space. To evaluate temporal and spatial variation in mutualistic networks, therefore, we evaluated the consistency of a nectarivory/pollination network across years, by season, and over space. We tracked nectaring interactions among 37 butterfly and 58 flowering plant taxa during an 11‐yr period (2007–2017), across each summer and over a grassland landscape in Pennsylvania, USA. The composition of butterflies, plants, and their interactions varied markedly across years, months, and sites. Despite this compositional variation, one metric of network structure, nestedness, was invariant, with interactions much more nested than random across all years, months, and sites. Together with previous studies, this result suggests ecological interaction networks are generally more nested than expected by chance. Other measures of network structure were more variable, especially over time. Numbers of plants and interactions varied by year, month, and site. Connectance and numbers of butterflies varied annually and seasonally. Temporal variation in specialization was also evident for some species at an annual level and for the community across the season. We further found highly stable species were almost always generalists, while highly specialized species were almost always temporally and spatially variable, with few exceptions. Together, these results suggest communities are comprised of a reliable core of generalist species, accompanied by a changing suite of specialist species that, when participating in the network, primarily interact with the reliable core species. Our finding of nested mutualistic network centered on stable‐generalist species accompanied by a changing suite of sporadic specialists indicates dynamic changes in ecological communities vary with topological position. This finding further suggests that, even as rare species are highly threatened by species invasion, climate change, and other anthropogenic perturbations, network structure may be robust to species loss and compositional change from these perturbations.

Study on microstructure mechanism of sandstone based on complex network theory

Rock contains a large number of micro-pores, which are of different shapes and complex structures. The structure information of sandstones is extracted based on different porosities through X-ray CT (Computer Tomography) scanning, photo processing techniques and complex network method to explore the topological structure of sandstone seepage network. The results show that sandstone seepage network has scale-free property. The minute quantities of pores with more throat connections have vital functions of overall connectivity of sandstone seepage network, while sandstone seepage network has strong robustness with random error. This research can provide reference for across scales research of porous seepage and multi-disciplinary application of complex network theory.

The Complex Network Theory-Based Urban Land-Use and Transport Interaction Studies

The research on complex networks offers novel insight into the analysis of complex urban systems. The objective of this article is to provide a review of complex network theory in urban land-use and transport studies to date. Some traditional integrated studies of urban land-use and traffic networks are summarized and analysed; related research gaps were proposed. Then, this paper reviewed the application of complex network theory in urban land-use and transport research and practice. It shows that the node importance identification method is critical for network protection or attack studies; the multiple centrality assessment and kernel density estimation approaches can be used to represent the intuitionistic connections of urban traffic networks and surrounding land-uses; it can be used to verify the changing trend and variation of landscape connectivity; also it can be applied to the identification of key changed land-use types in land-use cover change; the coevolution process can be treated as an integrated way to discuss the relationships between urban traffic network growth and land-use change, and the multilayer networks based analysis is a novel method to measure their interactions. This paper is essential in establishing apparent research interests and points out the further potential application of complex network theory in urban traffic network and land-use related studies.

A new approach for nuclear reactor analysis based on complex network theory

In this work, complex network theory is applied for the first time in the field of nuclear reactor physics to present a new approach for the evaluation of the multiplication factor of a nuclear system. The approach describes the random walk of a neutron in a network representative of the nuclear system. The network is constituted by multiple layers, each one representing a type of reaction (scattering, fission and capture) and each layer is constituted by different nodes, each one representing a different spatial position in the nuclear system. The probability of a neutron to jump from a node to another is governed by the material cross sections.Good agreement is achieved between the predictions of the proposed method and of Monte Carlo simulation.The outcome of this work constitutes a starting point for further research on the application of complex network theory to the field of nuclear reactor physics, while, at the same time, the experience retrieved from the application of complex networks may give useful insight for the improvement of classical approaches to nuclear reactor analysis.

A complex network community detection algorithm based on label propagation and fuzzy C-means

Community detection algorithms have important significance in the research and practical application of complex network theory. This paper proposes a community detection method by improved label propagation and fuzzy C-means. Due to low accuracy and instability detection results, we modify original label propagation framework. Primarily, initial labels of vertexes are assigned by neighbor evaluation method. Secondarily, the labels of vertexes with large diversity in each community are revised by fuzzy C-means membership vectors. Tertiarily, parameters are updated until communities status is stabilized ultimately. The results showed that this method can achieve better accuracy on synthetic and real network.

Multivariate financial time series in the light of complex network analysis

We established a complex network from multivariate financial time series in which one node represents the types of states corresponding to the combination of the fluctuations of the crude oil future prices, the S&P 500 Index, the US Dollar Index, and gold future prices on a given day; one edge denotes the transition time from one node to another; and the weight is the transition frequency between two states. Through analyzing the network’s topological structure, we obtain the characteristics of the transitions of these states in financial time series. The results show that nodes’ out-strength distribution and betweenness centrality distribution both follow the power-law distribution. A shock to one financial market can be quickly transited to the other three financial markets and a transition probability matrix is proposed to predict the short-term financial market fluctuations. The transition characteristics under volatility clustering of the network that are obtained in this study provide a new perspective to explain financial volatility clustering, which extends the application of complex network theory to financial studies and helps investors understand the financial market.

Analysis of the main active ingredients and bioactivities of essential oil from Osmanthus fragrans Var. thunbergii using a complex network approach

BackgroundOsmanthus fragrans has been used as folk medicine for thousands of years. The extracts of Osmanthus fragrans flowers were reported to have various bioactivities including free radical scavenging, anti-inflammation, neuroprotection and antitumor effects. However, there is still lack of knowledge about its essential oil.MethodsIn this work, we analyzed the chemical composition of the essential oil from Osmanthus fragrans var. thunbergii by GC-MS. A complex network approach was applied to investigate the interrelationships between the ingredients, target proteins, and related pathways for the essential oil. Statistical characteristics of the networks were further studied to explore the main active ingredients and potential bioactivities of O. fragrans var. thunbergii essential oil.ResultsA total of 44 ingredients were selected from the chemical composition of O. fragrans var. thunbergii essential oil, and that 191 potential target proteins together with 70 pathways were collected for these compounds. An ingredient-target-pathway network was constructed based on these data and showed scale-free property as well as power-law degree distribution. Eugenol and geraniol were screened as main active ingredients with much higher degree values. Potential neuroprotective and anti-tumor effect of the essential oil were also found. A core subnetwork was extracted from the ingredient-target-pathway network, and indicated that eugenol and geraniol contributed most to the neuroprotection of this essential oil. Furthermore, a pathway-based protein association network was built and exhibited small-world property. MAPK1 and MAPK3 were considered as key proteins with highest scores of centrality indices, which might play an important role in the anti-tumor effect of the essential oil.ConclusionsThis work predicted the main active ingredients and bioactivities of O. fragrans var. thunbergii essential oil, which would benefit the development and utilization of Osmanthus fragrans flowers. The application of complex network theory was proved to be effective in bioactivities studies of essential oil. Moreover, it provides a novel strategy for exploring the molecular mechanisms of traditional medicines.

Application of complex network theory to the recent foreshock sequences of Methoni (2008) and Kefalonia (2014) in Greece

Seismic hazard evaluation before recent strong main shocks in the area of Greece is attempted using prior seismicity on the basis of earthquake network theory. The connections of earthquake networks are constructed from successive earthquakes and the nodes are represented by cells of normal grids that were considered superimposed on the study areas. The dynamic evolution of the network structure is examined at sliding windows for identifying periods of statistically significant change, i.e., the network structure differentiation from that of a random network, where the structure is characterized by selected network measures, including the index of small-worldness property. By studying the structure of complex earthquake network, a distinct dynamic evolution is revealed, 2 months before the main shock occurrence. Particularly, the network measures, such as clustering coefficient and small-worldness index, tend to increase before and exhibit an abrupt jump at the time of the main shock occurrence, and then slowly decrease and become stable with small variations as before.

A Model for Coopetition Evolution of Software Industrial Virtual Cluster

The application of complex network theory can clearly reveal the coopetition activities between inter-members in software industrial virtual cluster. The research result shows that neither simplex cooperation nor competition，but it is a kind of dynamic equilibrium of coopetition. The empirical is analyzed by using the example of cloud computing. In the process of coopetition evolution, the distribution functions of node strength are from random to power law and eventually to random.

The Homogeneity Research of Urban Rail Transit Network Performance

Urban Rail Transit is an important part of the public transit, it is necessary to carry out the corresponding network function analysis. Previous studies mainly about network performance analysis of a single city rail transit, lacking of horizontal comparison between the multi-city, it is difficult to find inner unity of different Urban Rail Transit network functions. Taking into account the Urban Rail Transit network is a typical complex networks, so this paper proposes the application of complex network theory to research the homogeneity of Urban Rail Transit network performance. This paper selects rail networks of Beijing, Shanghai, Guangzhou as calculation case, gave them a complex network mapping through the L, P Space method and had a static topological analysis using complex network theory, Network characteristics in three cities were calculated and analyzed form node degree distribution and node connection preference. Finally, this paper studied the network efficiency changes of Urban Rail Transit system under different attack mode. The results showed that, although rail transport network size, model construction and construction planning of the three cities are different, but their network performance in many aspects showed high homogeneity.

Functional brain networks: great expectations, hard times and the big leap forward.

Many physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks has generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain. We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation of complex network theory, to account for its exquisitely complex functioning mode.

Complex Network Theory in the Application of Optimization Topology Network

Complex network theory is a new theory, which is rising with the rapid development of the computer. At present, the network structure of computers more complex network models already existed, has been unable to meet its topological properties. So, emerge as the times require complex network theory, provides a new development ideas and platform at the same time, complex network theory to the study of computer network topology. This paper firstly introduces the complex network theory, and then about the application of complex network theory in computer topology behavior, research on computer network topology and the experiment and model specific about complex network theory. More reduction.