The aim of the paper is to introduce some of the history and key concepts of network science to a philosophical audience, and to highlight a crucial—and often problematic—presumption that underlies the network approach to complex systems. Network scientists often talk of “the structure” of a given complex system or phenomenon, which encourages the view that there is a unique and privileged structure inherent to the system, and that the aim of a network model (...) is to delineate this structure. I argue that this sort of naïve realism about structure is not a coherent or plausible position, especially given the multiplicity of types of entities and relations that can feature as nodes and links in complex networks. (shrink)

First, a brief state of the art on the role of values in science is presented. The main points of the critique of the ideal of a free-value science are depicted (Sec. 2), followed by a framework of four phases in the development of scientific activity that allows us to better assess the influence of values in science. The basic thesis of the present text criticizes the conception close to Heather Douglas’ thesis, according to which values, including cognitive values, can (...) only play an indirect role in science (Sec. 3). In order to show that, at certain phases of science, the explanatory power (a cognitive value) can play a direct role, we propose an examination of structural explanation, as distinguished from mechanistic explanation, in a case of experimental linguistics related to language acquisition (Sec. 4). The conclusion is presented in the last section. (shrink)

Resumen En el texto se expone un proceso de modelación basado en dos consideraciones (Sec. 2): que los modelos son autónomos y que sus metas directas son al menos tres: estar bien construidos, adecuarse al mundo empírico y ser capaces de realizar tareas subrogatorias. Para ello, se esbozan varios ingredientes fundamentales de la tarea modeladora en la lingüística basada en evidencias, así como los de un marco formal elegido para representar aquellos. La tercera sección está dedicada a aplicar el presente (...) análisis al caso de las redes complejas con el fin de que conceptos clave como la validez, la fiabilidad y la replicabilidad de la modelación, así como el concepto de diseño experimental en RC, se puedan observar desde un prisma representacional. La sección cuarta refleja las capacidades aplicativas de (RC), especialmente en el caso del aprendizaje del lenguaje en casos atípicos y a menudo socialmente marginados. A modo de cierre, la conclusión resume la puesta en práctica de la herramienta RC. Abstract A modeling process is initially outlined based on two considerations (Sec. 2), namely that models themselves are autonomous and that their direct goals are at least threefold: to be well constructed, to fit the empirical world, and to be able to perform subrogative tasks. This section outlines the fundamental elements of the modeling task in evidence-based linguistics, as well as those of a formal framework chosen to represent the former. The third section is devoted to applying the present theoretical analysis to the case of complex networks (CN), so that key concepts such as validity, reliability and replicability of modeling, as well as the concept of experimental design in complex networks, can be viewed from a representational perspective. The fourth section reflects the application capabilities of CN, especially in the case of language learning in atypical and often socially marginalized cases. Finally, the conclusion summarizes the implementation of the CN-tool. (shrink)

In a complex network, each edge has different functions on controllability of the whole network. A network may be out of control due to failure or attack of some specific edges. Bridges are a kind of key edges whose removal will disconnect a network and increase connected components. Here, we investigate the effects of removing bridges on controllability of network. Various strategies, including random deletion of edges, deletion based on betweenness centrality, and deletion based (...) on degree of source or target nodes, are used to compare with the effect of removing bridges. It is found that the removing bridges strategy is more efficient on reducing controllability than the other strategies of removing edges for ER networks and scale-free networks. In addition, we also found the controllability robustness under edge attack is related to the average degree of complex networks. Therefore, we propose two optimization strategies based on bridges to improve the controllability robustness of complex networks against attacks. The effectiveness of the proposed strategies is demonstrated by simulation results of some model networks. These results are helpful for people to understand and control spreading processes of epidemic across different paths. (shrink)

In recent years, the method of the complex network has been applied to various fields. Dynamics research in complex networks is also an important branch. There are many types of research into dynamic complex network, but few scholars use bibliometrics to study it. Therefore, this paper adopts the method of bibliometrics to analyze the development history and status quo of dynamic complex network, providing a summary description of this research field. We used CiteSpace (...) and Pajek to analyze 2936 relevant papers on the website of Web of Science and further interpreted and summarized the analysis results. To fully show the bibliometric results, we further derive the productive countries, institutions, sources, and authors and some main research directions in the dynamic complex network research. The research results show that the dynamic complex network research method was applied to various fields after the dynamic research method had been integrated into the complex network. According to the subject analysis of the cited papers, we find that the dynamic complex network method has been widely used in various subjects. Through emergent analysis, we found that the latest hot research trends are the study of infectious diseases and applications in neural networks. At the same time, through the main path analysis, we find the essential literature to elaborate on the development context of a dynamic complex network at different time points. This paper gives a comprehensive analysis of dynamic complex network research and provides some enlightenment for the future research direction. (shrink)

As an efficient approach, the trust policy is implemented to defend against insider attacks in complex networks. However, the imperfection of trust relationships directly hinders the effort to quantitatively calculate trust value, especially in choosing a cooperative partner. Fortunately, the game theory is gradually concerned with addressing the above issue to further enhance security. In this paper, the game theory and the trust policy are reviewed briefly. Then, the research roadmap on game-based trust in complex networks is discussed (...) and analysed deeply. Furthermore, some research directions in the near future are given. It is worth mentioning that our contributions not only describe the evolution of the game-based trust clearly but also suggest the trust mechanism based on the bounded rational game more suitable to uncertain information. (shrink)

Previous research studies of traffic networks are mainly based on planar networks and less considered the influence of multilayer networks, which illustrate and represent different appropriate urban traffic modes. Development of rail and road networks is inseparable from the development of a prosperous urban area; thus, research on multilayer networks has scientific potential and fulfils a real need. In this paper, a framework of complex network based integrated multilayer urban growth and optimisation model is proposed, to analyse the (...) complex relationships between the traffic network structure, the population growth, and the urban land-use. The innovation of this paper is the combination of the traffic complex multilayer networks and the “Four Step Model”. With the multiobjective, multilayer network coevolution and optimisation model, a more efficient traffic network layout was generated based on different land-use, population density, and travel speed scenarios. Then, this paper has proved that the proposed CNIMUGOM can save the traffic network construction investment, reduce the travel cost, make the urban traffic network more efficient, and decrease the total traffic flow amount. This research has connected the recent complex multilayer network related study and traditional urban economic model based study. The findings of the study afford to improve the current land-use and traffic integrated models and can provide traffic network planning suggestions for urban agglomeration development. (shrink)

Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (...) problem. In the proposed method, we consider RA, LP, and CAR indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods. (shrink)

Though complex networks have been widely applied in the research of chemistry, there is hardly any introduction about the establishment of networks using chemical bonds. In this paper, we consider chemical elements as a system linked by chemical bonds and create the undirected chemical bond network by abstracting nodes from elements and undirected edges from bonds. Connectivity, heterogeneity, small world and disassortativity of this network show the macro structural rationality of this system. The degree and k-order neighbors (...) of an element, which represent the micro topology of this network, can be used to measure its chemical reactivity and detect how many kinds of compounds it can form. The similarity between two elements is measured by the Jaccard index and the VOS mapping technique, results of which are similar to well-known similarities between elements shown by the periodic table. The establishment and topological analysis of this network provide another way to understand and study elements and bonds, and more chemical properties of elements and bonds can be studied by complex networks. (shrink)

Psychometrics has always been confronted with fundamental criticism, highlighting serious insufficiencies and fallacies. Many fallacies persist, however, because each critic explores only some fallacies while still building on others. This article scrutinizes the epistemological, metatheoretical, and methodological foundations of psychometrics, revealing a complex network of numerous conceptual fallacies underlying its framework of theory and practice. At its core lies a key challenge for psychology: the necessity to distinguish the phenomena under study from the means used to explore them (...) (e.g., concepts, methods, data). This distinction is intricate because concepts constitute psychical phenomena in themselves and many psychical phenomena are accessible only through language-based methods. The analyses show how insufficient consideration of this important distinction and common misconceptions about concepts and language (e.g., signifier–referent conflation, reification of constructs) led to conflations of disparate notions of key terms in psychological measurement (e.g., “variables”, “attributes”, “causality”) and numerous interrelated fallacies (e.g., construct–referent conflation, phenomenon–quality–quantity conflation, numeral–number conflation). These fallacies are maintained and masked by repeated conceptual back-and-forth switching between two incompatible epistemological frameworks, (a) an operationist framework of data modeling implemented through methodical and statistical operations and (b) a realist framework of measurement sporadically invoked in theoretical considerations but neither theoretically elaborated nor empirically implemented. The analyses demonstrate that psychometrics constitutes only data modeling but not data generation or even measurement as often assumed and that analogies to (indirect or fundamental) physical measurement are mistaken. They provide theoretical support for the increasing criticism of psychometrics and its use in research and applied contexts. (shrink)

The continuous increase of carbon emissions is a serious challenge all over the world, and many countries are striving to solve this problem. Since 2020, a widespread lockdown in the country to prevent the spread of COVID-19 escalated, severely restricting the movement of people and unnecessary economic activities, which unexpectedly reduced carbon emissions. This paper aims to analyze the carbon emissions data of 30 provinces in the 2020 and provide references for reducing emissions with epidemic lockdown measures. Based on the (...) method of time series visualization, we transform the time series data into complex networks to find out the hidden information in these data. We found that the lockdown would bring about a short-term decrease in carbon emissions, and most provinces have a short time point of impact, which is closely related to the level of economic development and industrial structure. The current results provide some insights into the evolution of carbon emissions under COVID-19 blockade measures and valuable insights into energy conservation and response to the energy crisis in the post-epidemic era. (shrink)

Discussion about project management, in both the academic literature and industry, is predominantly based on theories of control, many of which have been developed since the 1950s. However, issues arise when these ideas are applied unilaterally to all types of projects and in all contexts. In complex environments, management problems arise from assuming that results, predicted at the start of a project, can be sufficiently described and delivered as planned. Thus, once a project reaches a critical size, a calendar, (...) and a certain level of ambiguity and interconnection, the analysis centered on control does not function adequately. Projects that involve complex situations can be described as adaptive complex systems, consistent in multiple interdependent dynamic components, multiple feedback processes, nonlinear relations, and management of hard data and soft data. In this study, through a complex network, the dynamic structure of a project and its trajectories are simulated using inference processes. Finally, some numerical simulations are described, leading to a decision making tool that identifies critical processes, thereby obtaining better performance outcomes of projects. (shrink)

The approach of the complex network has well described seismic complex systems. In this paper, this is the first time three classical network construction methods for seismicity are compared. By using the same dataset from the Southern California Seismic Network, three networks are constructed. They all present the scale-free, small-world properties, a strength-degree correlation, and an assortative mixing feature. However, they show some differences in the hierarchical clustering feature. On observing the evolution results, three measures (...) show a similar correlation with seismicity dynamics, but one measure shows a different result. These results show that different network construction methods will present some similarities and differences in network properties. This situation needs to be considered, especially when discussing a predictive indicator of seismicity. (shrink)

Most growth models for complex networks consider networks comprising a single connected block or island, which contains all the nodes in the network. However, it has been demonstrated that some large complex networks have more than one island, with an island size distribution obeying a power-law function Is~s-α. This paper introduces a growth model that considers the emergence of islands as the network grows. The proposed model addresses the following two features: the probability that a new (...) island is generated decreases as the network grows and new islands are created with a constant probability at any stage of the growth. In the first case, the model produces an island size distribution that decays as a power-law Is~s-α with a fixed exponent α=1 and in-degree distribution that decays as a power-law Qi~i-γ with γ=2. When the second case is considered, the model describes island size and in-degree distributions that decay as a power-law with 2<α<∞ and 2<γ<∞, respectively. (shrink)

Cooperative spreading dynamics on complex networks is a hot topic in the field of network science. In this paper, we propose a strategy to immunize some nodes based on their degrees. The immunized nodes disable the synergistic effect of cooperative spreading dynamics. We also develop a generalized percolation theory to study the final state of the spreading dynamics. By using the Monte Carlo method, numerical simulations reveal that immunizing nodes with a large degree cannot always be beneficial for (...) containing cooperative spreading. For small values of transmission probability, immunizing hubs can suppress the spreading, while the opposite situation happens for large values of transmission probability. Furthermore, numerical simulations show that immunizing hubs increase the cost of the system. Finally, all numerical simulations can be well predicted by the generalized percolation theory. (shrink)

Dragon boat sport is a traditional activity in China. In recent years, dragon boat sport has become more and more popular around the world. In order to face more challenges, it is urgent for athletes to enhance their own strength. Scientific training methods are particularly important for athletes, and accurate training data are the basis to support scientific training. Traditional mathematical statistic methods neither can sample signals accurately nor can they do real-time analysis and feedback the characteristics to each athlete. (...) In this paper, we use the wearable device with a triaxial accelerometer and heart rate sensor builtin to sample the speed signals and heart rate signals of athletes in various stages of men’s 1000m straight race. Based on the complex network theory, we regard the 23 dragon boat athletes in the dragon boat race as 23 nodes so as to establish a network with 23 nodes and reflect the importance of nodes by measuring the impact of node deletion on the results of the race. The neural network multilayer perceptron model is used for training to obtain the optimal combined value with speed and heart rate for each race stage. The optimal value will be used in the simulated race as the target value to verify if it can help to improve the training efficiency. Experimental results show that the optimal value obtained by this method has a positive effect on the results of the dragon boat race which is beneficial to sports training and tactics planning. (shrink)

Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. A valuable number of approaches have been developed and deployed in these areas where just a few have used centrality measures along with their concerning deficiencies and limitations in their studies. Therefore, to resolve these challenging issues, we propose a novel effective distance-based centrality algorithm for the identification of influential nodes in (...) concerning networks. EDBC algorithm comprises factors such as the power of K-shell, degree nodes, effective distance, and numerous levels of neighbor’s influence or neighborhood potential. The performance of the proposed algorithm is evaluated on nine real-world networks, where a susceptible infected recovered epidemic model is employed to examine the spreading dynamics of each node. Simulation results demonstrate that the proposed algorithm outperforms the existing techniques such as eigenvector, betweenness, closeness centralities, hyperlink-induced topic search, H-index, K-shell, page rank, profit leader, and gravity over a valuable margin. (shrink)

Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This study proposed the Sigmoid Fish Swarm Optimization algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement (...) and community detection. The proposed SiFSO algorithm’s performance is tested against state-of-the-art particle swarm optimization algorithms in Q-modularity and normalized mutual information. The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms. (shrink)

The policy of government procurement of elderly care services has the vulnerability characteristics that all complex systems have. To maintain the policy’s robustness, this paper studies the vulnerability of government procurement of elderly care services from the perspective of complex network. Case analysis and sample statistics are used to obtain the vulnerability influencing factors of the policy. Then, complex network diagram of vulnerability influencing factors is constructed through Pajek software. The compatibility coefficient is used to (...) investigate the network’s overall vulnerability from a macro perspective. From a micro perspective, a vulnerability index of the complex network is applied to measure each node’s vulnerability value, and the vulnerability priority of the nodes is established. Results show that government purchasing power and supervisory bodies’ diversity are at the first priority. The remaining vulnerability influencing factors are at the second and the third priority. (shrink)

Gene expression network is also a type of complex network. It is challenging to analyze the gene expression network through relevant knowledge and algorithms of a complex network. In this paper, the existing characteristics of genes are analyzed from various indexes of the gene expression network to analyze key genes and TOP genes. Firstly, gene chip data are screened, gene data with obvious characteristics are selected, and relevant clustering characteristics are analyzed. Then, the (...) complex gene network structure is established, and gene networks with different threshold shapes and different sizes are selected. Finally, the relevant indexes and PR values after the PageRank algorithm are analyzed for complex networks under different thresholds, thus establishing the TOP gene and PR sequence. (shrink)

This work analyzes and characterizes the spread of the COVID-19 disease in Mexico, using complex networks and optimization approaches. Specifically, we present two methodologies based on the principle of the rupture for the GC and Newton's law of motion to quantify the robustness and identify the Mexican municipalities whose population causes a fast spread of the SARS-CoV-2 virus. Specifically, the first methodology is based on several characteristics of the original version of the Vertex Separator Problem, and the second is (...) based on a new mathematical model. By solving VSP, we can find nodes that cause the rupture of the giant component. On the other hand, solving the NLM can find more influential nodes for the entire system’s development. Specifically, we present an analysis using a coupled social network model with information about the main characteristics of the contagion and deaths caused by COVID-19 in Mexico for 19 months. This work aims to show through the approach of complex networks how the spread of the disease behaves, and, thus, researchers from other areas can delve into the characteristics that cause this behavior. (shrink)

Consider chemical elements as a system, we create an undirected chemical network with 99 elements and 1916 edges from Chemspider, a website that provide search engines to collect compounds. Using this network and the network that we used in our previous work with 97 elements and 2198 edges, we found that RootedPageRank, a link prediction tool in complex network, can be used to predict potential binary compounds, because the changing trend of PageRank probability of each (...) element in these networks all follow the periodic law, despite of the difference of scale of these networks. The accuracy test indicates that at least 7 among top 10 predicted compoundss in one network can be verified using the compoundss in the other network or in other chemical database, proving that this method can be used to provide guidance in finding potential binary compounds, suggesting that we can study chemical properties from the view of complex network. (shrink)

This paper uses the 5-five-minute high-frequency data of energy-listed companies in China's A-share market to extract the jump of energy stock prices and build a dynamic stock price jump complex network. Then, we analyze the clustering effect of the complex network. The research shows that the energy stock price jump is an important part of stock price volatility, and the complex network of energy stock jump risk has obvious time-varying characteristics. However, the infection problem (...) of stock price jump risks needs specific analysis. China's coal industry has an important influence on the development of China's energy industry. According to the clustering analysis results of the network community, the clustering effect of the network community has time-varying characteristics. After October 2017, the clustering effect of the jumping risk of the coal industry and the new energy industry is obvious. The risk contagion within the new energy industry community is a key point for the development of the new energy industry. (shrink)

Urban rail transit can improve a city’s accessibility. However, high construction and operation costs restrict the development of urban rail transit. Value capture recoups the additional value that the investments of urban rail transit confer to local land and is considered to be an effective measure to alleviate this financial problem. Understanding the land value uplift effects of urban rail transit is essential for understanding value capture. This study applied a Space-P model of urban rail transit network based on (...) complex network theory and demonstrated the influence of urban rail transit network characteristics on residential and commercial land prices. The model was tested with eight metropolises in China, using the 2003 to 2022 timeframe as the context. The results showed a significant positive correlation between the number of nodes and the land prices, the average clustering coefficient was highly positively correlated with the land prices, and there was a significant negative correlation between the average path length and the land prices. This study provides theoretical support for value capture, is beneficial for urban rail transit planning, and supports improvements in the development quality of urban rail transit networks. (shrink)

With the contradiction between supply and demand being intensified, the unreasonable crude oil trade structure has become increasingly prominent. Therefore, optimizing the supply and demand structure of the global crude oil trade is an urgent problem, which has become one of the crucial factors affecting the energy strategy and economic development of each country. This paper builds an optimization model that aims at minimizing crude oil trade costs based on the complex network theory. Meanwhile, an optimal solution generation (...) approach is proposed and developed in this paper. Compared with the preoptimized trade network, the proposed model can effectively reduce the trade cost. By topological analysis of the trade network and its optimal configuration, we obtain that both the preoptimized and optimized crude oil trade networks follow power-law distribution. By using the minimum spanning tree, we find that the major crude oil net exporting countries have the most significant influence and are at the core in the optimized trade structure. This work focuses on the sustainable development of the global crude oil trade and provides a fresh perspective for the optimal crude oil trade system. Moreover, the methodology and model may be applied in the investigation of optimization for other energy system structures. (shrink)

As one of the most important routing problems in the complex network within a very-large-scale integration circuit, bus routing has become much more challenging when witnessing the advanced technology node enters the deep nanometer era because all bus bits need to be routed with the same routing topology in the context. In particular, the nonuniform routing track configuration and obstacles bring the largest difficulty for maintaining the same topology for all bus bits. In this paper, we first present (...) a track handling technique to unify the nonuniform routing track configuration with obstacles. Then, we formulate the topology-aware single bus routing as an unsplittable flow problem, which is integrated into a negotiation-based global routing to determine the desired routing regions for each bus. A topology-aware track assignment is also presented to allocate the tracks to each segment of buses under the guidance of the global routing result. Finally, a detailed routing scheme is proposed to connect the segments of each bus. We evaluate our routing result with the benchmark suite of the 2018 CAD Contest. Compared with the top-3 state-of-the-art methods, experimental results show that our proposed algorithm achieves the best overall score regarding specified time limitations. (shrink)

We study the synchronization of complex networks by using event-sampling information. The nodes of the network are connected with event-triggered communication via multiple couplings. The couplings are split into several channels. Not all the channels are connected. Only a part of the states of each node can be communicated by the channels. An event detector is designed for each channel to independently determine the sampling moments. The couplings of the network are partial and event-triggered. Both features make (...) that less information can be used for synchronization. The pinning controllers are also designed based on the sampled information. By establishing a time-dependent Lyapunov functional and utilizing an efficient event condition, we derive less conservative criteria for the synchronization of complex networks. Finally, the effectiveness of our main results is verified by an illustrative example, and comparisons are also presented to show how much conservatism can be reduced. (shrink)

Knowledge has become an important resource that can help enterprises gain a competitive advantage in the market. In this regard, knowledge sharing in the process of cooperative innovation provides an important way to acquire knowledge. With the development of innovation, the innovation network has become an important carrier of knowledge sharing, which can also have an influence on knowledge sharing. Based on knowledge management theory and complex network theory, this study constructed a multilayer network environment for (...) knowledge sharing. It then identified the network elements and knowledge-sharing paths that influence knowledge sharing from microperspectives and macroperspectives. On this basis, the effects of node cohesion and weak connection on knowledge sharing in small-world and scale-free topologies were analyzed by computer simulation. The results showed that, in an innovation network with a scale-free topology, cohesion and weak ties had a great influence on the average knowledge level and knowledge equilibrium. Meanwhile, in small-world topological innovation networks, a mixed path had the best promotion effect on network knowledge sharing. (shrink)

The cultural element is the minimum unit of a cultural system. The systematic categorizing, organizing, and retrieval of the traditional Chinese cultural elements are essential prerequisites for the realization of effective extracting and rational utilization, as well as the prerequisite for exploiting the contemporary value of the traditional Chinese culture. To build an objective, integrated, and reliable classification method and a system of traditional Chinese cultural elements, this study takes the text of Taiping Imperial Encyclopedia in Northern Song Dynasty as (...) the primary data source. The unsupervised word segmentation methods are used to detect Out-of-Vocabulary, and then the segmentation results by the THULAC tool with and without custom dictionary are compared. The TF-IDF algorithm is applied to extract the keywords of cultural elements and the Ochiia coefficient is introduced to create complex networks of traditional Chinese cultural elements. After analyzing the topological characteristics of the network, the community detection algorithm is used to identify the topics of cultural elements. Finally, a “Means-Ends” two-dimensional orthogonal classification system is established to categorize the topics. The results showed that the degree distribution in the complex network of Chinese traditional cultural elements is a scale-free network with γ = 2.28. The network shows a structure of community and hierarchy features. The top 12 communities have taken up to 91.77% of the scale of the networks. Those 12 topics of the traditional Chinese cultural elements are circularly distributed in the orthogonal system of cultural elements’ categorization. (shrink)

The dynamic models are proposed to investigate the influence node activity has on rumor spreading process in both homogeneous and heterogeneous networks. Different from previous studies, we believe that the activity of nodes in complex networks affects the process of rumor spreading. An active node can have contact with all the nodes it directly links to, while an inactive node could only interact with its active neighbors. We explore the joint effort of activity rate, spreading rate and network (...) topology on rumor spreading process by mean-field equations and numerical simulations, which reveals that there exists a critical curve consisting of critical activity rate and spreading rate; meanwhile, activity rate and spreading rate both have influence on the final rumor spreading scale. (shrink)

This article is related to the issue of fixed-time synchronization of different dimensional complex network systems with unknown parameters. Two suitable adaptive controllers and dynamic parameter estimations are proposed such that the complex network driving and response systems can be synchronized in the settling time. Based on fixed-time control theory and Lyapunov functional method, novel sufficient conditions are provided to guarantee the synchronization within the fixed times, and the settling times are explicitly evaluated, which are independent (...) of the initial synchronization errors. Finally, a numerical example is given to illustrate the effectiveness of the proposed control algorithms. (shrink)

This extended study presents a hybridization of particle swarm optimization with complex network construction and analysis. A partial population restart is performed in certain moments of the run of the algorithm based on the information obtained from a complex network analysis. The complex network structure represents the communication in the population. We present experimental results of the method alongside with statistical evaluation and discuss future possibilities of this approach. The main goal of the work (...) is not to propose a new highly competitive PSO variant but to present the possibility of using the unconventional tool as an alternative to conventional diversity measures. The main benefit of the network analysis is that it has same-time requirements regardless of the dimension of the problem. (shrink)

This paper is concerned with the outer exponential synchronization of the drive-response complex dynamical networks subject to time-varying delays. The dynamics of nodes is complex valued, the interactions among of the nodes are directed, and the two coupling matrices in the drive system and the response system are also different. The intermittent pinning control is proposed to achieve outer exponential synchronization in the aperiodical way. Some novel sufficient conditions are derived to guarantee outer exponential synchronization of the considered (...) complex-valued complex networks by using the Lyapunov functional method. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed control protocols. (shrink)

Flight delay is one of the most challenging threats to operation of air transportation network system. Complex network was introduced into research studies on flight delays due to its low complexity, high flexibility in model building, and accurate explanation about real world. We surveyed recent progress about flight delay which makes extensive use of complex network theory in this paper. We scanned analyses on static network and temporal evolution, together with identification about topologically important (...) nodes/edges. And, we made a clarification about relations among robustness, vulnerability, and resilience in air transportation networks. Then, we investigated studies on causal relations, propagation modellings, and best spreaders identifications in flight delay. Ultimately, future improvements are summarized in fourfold. Under Complex Network, flight operation relevant subsystems or sublayers are discarded by the majority of available network models. Hierarchical modelling approaches may be able to improve this and provide more capable network models for flight delay. Traffic information is the key to narrow the gap between topology and functionality in current situations. Flight schedule and flight plan could be employed to detect flight delay causalities and model flight delay propagations more accurately. Real flight data may be utilized to validate and revise the detection and prediction models. It is of great importance to explore how to predict flight delay propagations and identify best spreaders at a low cost of calculation complexity. This may be achieved by analyzing flight delay in frequency domain instead of time domain. Summation of most critical nodes/edges may not be the most crucial group to network resilience or flight delay propagations. Effective algorithm for most influential sequence is to be developed. (shrink)

The complex activity of the human brain makes it difficult to get a big picture of how the brain works and functions as the mind. We examine pertinent studies, as well as evolutionary and embryologic evidence to support our theoretical model consisting of separate but interactive layers of human neural activity. The most basic layer involves default mode network (DMN)activity and cardiorespiratory oscillations. We propose that these oscillations support other neural activity and cognitive processes. The second layer involves (...) limbic system activity accompanied by corresponding changes in cardiorespiratory oscillations. The third layer consists of corticothalamic processing and involves higher cortical functions including awareness, cognition,and consciousness. These layers interact to form the complex neural activity of the human brain. Examining the origins and relationships of various neural and physiologic oscillations may provide better understanding of human neurophysiology and consciousness. (shrink)

Social network theory is an important paradigm of social structure research, which has been widely used in various fields of research. This paper reviews the development process and the latest progress of social network theory research and analyzes the research application of social network. In order to reveal the deep social structure, this paper analyzes the structure of social networks from three levels: microlevel, mesolevel, and macrolevel and reveals the origin, development, perfection, and latest achievements of (...) class='Hi'>complex network models. The regular graph model, P1 model, P2 model, exponential random graph model, small-world network model, and scale-free network model are introduced. In the end, the research on the social network structure is reviewed, and social support network and social discussion network are introduced, which are two important contents of social network research. At present, the research on social networks has been widely used in coauthor networks, citation networks, mobile social networks, enterprise knowledge management, and individual happiness, but there are few research studies on multilevel structure, dynamic research, complex network research, whole network research, and discussion network research. This provides space for future research on social networks. (shrink)