Complex Network Applications Research Articles

Vacancy-driven resistive switching behavior based on wafer-scale MoSe2 artificial synapses

Recently, researches have revealed that synaptic transistors based on two-dimensional materials (2DMs) have substantial advantages and potential for modeling neural synapses. However, the mechanism of artificial synaptic device with 2DMs as conducting channels remains to be further clarified. Here, high-quality wafer-scale MoSe2 nanosheets were achieved by ALD-CVD process, where further 23 × 23 × 4 synaptic arrays were fabricated in situ. Notably, the outstanding MoSe2 synaptic device exhibits an on/off ratio of 3.6 × 107 with hysteresis ratio of 6.8k and simulates crucial biological synaptic behaviors such as paired pulse facilitation and excitatory postsynaptic currents. The mechanism of Se vacancy induction and horizontal diffusion driven is revealed based on high-resolution transmission electron microscopy characterization with first-principles calculations, which suggests a possible mechanistic explanation for the resistive switching behavior of 2DMs transistors. Artificial synaptic devices with 2DMs based on the working mechanism of vacancy diffusion are capable of satisfying the needs of complex neural network applications.

An application of complex networks on predicting the behavior of infectious disease on campus

Networks are widely used in understanding the spread of infectious disease. Universities and colleges are characterized by frequent social gatherings and extensive interpersonal communication, making them ideal applications of complex networks. In this paper, a compound model consisting of a homogeneous (small world) network and a heterogeneous (scale free) network is established, the spreading characteristics of epidemics are analyzed by using the mean‐field (MF) and the heterogeneous mean‐field (HMF) approaches, and the effects of various factors such as the total node number (N), the randomly reconnection probability (p), the initial proportion of infected node (p0), the average degree (k), and the shared nodes (s) are simulated by numerical calculations. The simulation results are in consistent with theoretical predictions, indicating that the randomness effect decreases with increasing k and N and is small when k (k = 8) and N (N = 200) are relatively large; the randomness effect is more significant in the scale free (SF) network than in the small world (SW) network, and the disease spread faster in the SF network; shared nodes can weakly increase the disease spread, but when the shared nodes are involved in an infected and an uninfected network, it can induce the disease outbreak in the uninfected network.

Exploring community detection methods and their diverse applications in complex networks: a comprehensive review

Exploring community detection methods and their diverse applications in complex networks: a comprehensive review

Biological neuron modeling based on bifunctional memristor and its application in secure communication

In recent years, memristors have shown great potential in building artificial neural networks. Many researchers have worked on improving and innovating the design of memristors. The bifunctional memristor proposed in this paper provides new ideas for establishing artificial neural networks. And a novel second-order memristor Hindmarsh–Rose neuron (SOM-HR) is constructed on this basis. The SOM-HR model can well simulate the dynamical behavior of the memristive autapse HR neuron under electromagnetic radiation (EMR), and many types of firing patterns, coexisting firing patterns and state transition are found. In addition, the correctness of the numerical simulation is verified by circuit simulation and digital signal processor (DSP) implementation. Finally, the chaotic sequences generated by this model are used for image encryption, which guarantees the security of the encryption scheme. The results of the study provide a reference for construction and application of complex neural network.

A comprehensive survey on community detection methods and applications in complex information networks

A comprehensive survey on community detection methods and applications in complex information networks

Building a stable and robust anti-interference DNA dissipation system by eliminating the accumulation of systemic specified errors

BackgroundThe emergence of DNA nanotechnology has enabled the systematic design of diverse bionic dissipative behaviors under the precise control of nucleic acid nanodevices. Nevertheless, when compared to the dissipation observed in robust living systems, it is highly desirable to enhance the anti-interference for artificial DNA dissipation to withstand perturbations and facilitate repairs within the complex biological environments. ResultsIn this study, we introduce strategically designed “trash cans” to facilitate kinetic control over interferences, transforming the stochastic binding of individual components within a homogeneous solution into a competitive binding process. This approach effectively eliminates incorrect binding and the accumulation of systemic interferences while ensuring a consistent pattern of energy fluctuation from response to silence. Remarkably, even in the presence of numerous interferences differing by only one base, we successfully achieve complete system reset through multiple cycles, effectively restoring the energy level to a minimum. SignificanceThe system was able to operate stably without any adverse effect under conditions of irregular interference, high-abundance interference, and even multiplex interferences including DNA and RNA crosstalk. This work not only provides an effective paradigm for constructing robust DNA dissipation systems but also greatly broadens the potential of DNA dissipation for applications in high-precision molecular recognition and complex biological reaction networks.

Comparison Link Function from Summer Rainfall Network in Amazon Basin

The Amazon Basin is the largest rainforest in the world, and studying the rainfall in this region is crucial for understanding the functioning of the entire rainforest ecosystem and its role in regulating the regional and global climate. This work is part of the application of complex networks, which refer to a network modeled by graphs and are characterized by their high versatility, as well as the extraction of key information from the system under study. The main objective of this article is to examine the precipitation system in the Amazon basin during the austral summer. The networks are defined by nodes and connections, where each node represents a precipitation time series, while the connections can be represented by different similarity functions. For this study, three rainfall networks were created, which differ based on the correlation function used (Pearson, Spearman, and Kendall). By comparing these networks, we can identify the most effective method for analyzing the data and gain a better understanding of rainfall’s spatial structure, thereby enhancing our knowledge of its impact on different Amazon basin regions. The results reveal the presence of three important regions in the Amazon basin. Two areas were identified in the northeast and northwest, showing incursions of warm and humid winds from the oceans and favoring the occurrence of large mesoscale systems, such as squall lines. Additionally, the eastern part of the central Andes may indicate an outflow region from the basin with winds directed toward subtropical latitudes. The networks showed a high level of activity and participation in the center of the Amazon basin and east of the Andes. Regarding information transmission, the betweenness centrality identified the main pathways within a basin, and some of these are directly related to certain rivers, such as the Amazon, Purus, and Madeira. Indicating the relationship between rainfall and the presence of water bodies. Finally, it suggests that the Spearman and Kendall correlation produced the most promising results. Although they showed similar spatial patterns, the major difference was found in the identification of communities, this is due to the meridional differences in the network’s response. Overall, these findings highlight the importance of carefully selecting appropriate techniques and methods when analyzing complex networks.

Guest Editorial Complex Network Analysis and Applications in Next-Generation Consumer Electronics

Guest Editorial Complex Network Analysis and Applications in Next-Generation Consumer Electronics

Spatial clustering and spillover pathways analysis of O3, NO2, and CO in eastern China during 2017–2021

The eastern China presented the most serious O3 pollution and increasingly prominent regional characteristics. To understand the transport characteristics of O3 and its precursors and identify their potential relationships are of great guiding significance for interregional joint prevention and control. In this study, the annual and seasonal transport networks of O3 and its precursors (NO2 and CO) during 2017–2021 were constructed by applying the complex network method to air quality observations. And the key spatial clusters, the spillover paths and the potential links among pollutants were comprehensively analyzed based on the topological characteristic analysis of the established air pollutant transport networks. As the results showed, O3 pollution in the eastern China was affected by active regional transports of O3 and its precursors. Regional transports of O3, NO2, and CO were more prominent in autumn and showed high synchronization. The regional transport of precursors, especially NOx, was an important cause of regional O3 pollution. Air pollutant transport characteristics varied with seasons and regions, which demonstrating the importance of regulating seasonal and regional differentiated joint prevention and control strategies, especially for NOx. The results of this study can provide science-based guidance for the regional cooperative control of O3 pollution in the eastern China, and the application of complex networks can also provide a new methodological perspective for the study of air pollution transmission.

Investigation of the Global Stock Trading Based on Visibility Graph and Entropy Weight Method

The increasing complexity and dynamics of the stock trading market are major challenges for the financial industry and are primary dilemmas for all countries nowadays. In addition, the stock trading market has a considerable impact on the global economy, and its importance is self-evident. To cope with the complexity and dynamics of a stock trading market, this paper applies complex network theory and model to explore the topology of the global stock trading network. First, this paper collects stock trading data from 74 countries from 1999 to 2020. It converts the collected stock trading data of these countries into a complex network using a type of algorithm based on the time series visibility graph (VG) algorithm. Then, the data are analyzed by a complex network model, and six analytical metrics are obtained. Finally, the six metrics are analyzed by the entropy weight method to identify the key nodes in the network and to obtain the ranking of each country’s stock trading data. This paper is an effective application of complex network and entropy weight method in stock trend analysis, which mainly includes two contributions. First, the VG algorithm provides a novel research perspective for modeling the global stock trading trend. Second, key nodes in the network are analyzed and identified based on the entropy weight method, and the ranking of key nodes in the stock trading network is obtained, which provides a new method for further research on the stock trading trend, investment portfolio, and stock return forecasting.

Spectrum of prism graph and relation with network related quantities

<abstract><p>Spectra of network related graphs have numerous applications in computer sciences, electrical networks and complex networks to explore structural characterization like stability and strength of these different real-world networks. In present article, our consideration is to compute spectrum based results of generalized prism graph which is well-known planar and polyhedral graph family belongs to the generalized Petersen graphs. Then obtained results are applied to compute some network related quantities like global mean-first passage time, average path length, number of spanning trees, graph energies and spectral radius.</p></abstract>

Partial Discharge Detection by Edge Computing

Edge computing is becoming a mainstream platform for practical applications of machine learning and in particular deep learning. Many systems capable of efficient execution of deep neural models in the context of edge computing are readily available or beginning to appear on the consumer market. The Jetson platform from NVIDIA, the Neural stick from Intel, and the Edge TPU designed by Google are examples of devices that enable the application of complex neural networks in edge computing. This work investigates the ability of selected edge devices to address a real-world classification problem from electrical power engineering. It consists of the detection of partial discharges (PDs) from covered conductors (CCs) on high-voltage power lines. The CCs are used in heavily forested and generally inaccessible areas where clearance zones cannot be maintained. Detection of PDs can prevent forest fires and other disasters potentially caused by prolonged contact of CCs with vegetation. The problem is suitable for an edge computing-based solution because Internet connectivity in remote areas is usually insufficient and a 2G (GSM) mobile network is available at best. Because such locations are difficult to access and usually without a suitable power supply, the proposed solution puts an emphasis also on PD detection latency and the associated power consumption. Two principal approaches to PD detection are considered. One is based on the classification of 1D time series (raw data). The second approach uses the signal transformed into a 2D spectrogram. In this case, two types of algorithms are evaluated. The first one is a novel custom stacking ensemble detector composed of 2D convolutional neural networks and a neural meta-learner on top of it. The second one uses the well-known and widely-used used ResNet deep neural model.

The impact of default on the evolving dynamic networks of debtor-creditor relationships

The aim of this study was to study the underlying structural characteristics of networks formed by debtor-creditor relationships. According to attributes of P2P lending, this paper model the networks of debtor-creditor relationships as an evolving network with the addition and deletion of nodes. It was found that networks of debtor-creditor relationships are scale-free networks. When the impact of default was considered in networks of debtor-creditor relationships, it was found that the power-law distribution of degree is not changed by the event of default, but the exponent of power-law under the event of default is less than the situation without the event of default. Our results enrich the application of complex networks.

Towards assessing the importance of individual stations in hydrometric networks: application of complex networks

Towards assessing the importance of individual stations in hydrometric networks: application of complex networks

Coordinated cyber-physical attack considering false overload of lines

The application of many sensor devices and complex information communication networks in a power system brings new potential threats to the electrical cyber-physical system. In this paper, a coordinated cyber-physical attack method considering false line overload is proposed. Through the combination of physical side attack and information side attack, the false data injection attack is used to cover up the physical attack and create false overload events, leading the dispatcher to make unreasonable scheduling strategies to achieve the purpose of affecting the economic operation of the system. In response to false overload events, this paper proposes a method to select the target line of false overload. At the same time, the output cost of the generator, the cost of load reduction and the number of overload lines are used as the evaluation indices to measure the damage capability of the coordinated attack, and the evaluation indices of different dimensions are normalized using fuzzy methods. Finally, the system losses caused by different attack modes are compared and analyzed through simulation calculations, and the effectiveness of the proposed model is verified.

On the practical stability with regard to a part of the variables for distribution-dependent SDEs driven by time-changed Brownian motion

Distribution-dependent stochastic differential equations (DDSDEs, in short), also called as McKean–Vlasov SDEs, are a special class of SDEs and have some concrete applications in mean-field control, mean-field games and complex networks. Stability plays fundamental role among the theory and applications for DDSDEs. In this paper, we discuss a class of distribution-dependent stochastic differential equations driven by time-changed Brownian motions (TDDSDEs, in short). The main aim of this paper is to establish the stability for TDDSDEs. Sufficient conditions are provided to guarantee the solutions to be stable in different senses with regard to a part of the variables in terms of a Lyapunov function. Two examples are given to show the usefulness of the obtained theoretical results.

The role of influential nodes and their influence domain in community detection: An approximate method for maximizing modularity

Community detection is one way to reduce the complexity of analyzing networks, especially with their rapid growth. Dividing networks into communities can help analysts and experts to understand the behavior and function of the networks. Also, besides the community structure, finding the influential nodes to spread information in the networks is a critical issue for researchers. Community detection is a challenging topic in network science and, various methods have been proposed for that. Many methods that find community structure use modularity as a measure to qualify the strength of community structure. These methods try to find community structures based on maximizing modularity. Modularity maximization is an NP-hard problem. One of the approaches that could solve such problems is approximate algorithms. Identifying the influential nodes which has many applications in complex networks can also be used in community detection. Therefore to maximize the modularity, in this paper, we first try to identify influential nodes, and then by estimating their influence domain, the communities are detected. We used scale-free networks concepts to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods in community detection algorithms. In addition, our proposed method also identifies the most influential node within each community.

Understanding the situation at a glance: The powerful value added of complex networks to analyse humanitarian operations

Information management is considered as a key success factor in many fields. For disaster relief operations, where resources are scarce and often overwhelmed by the magnitude of the needs of affected people, the process of collecting and using data has been overseen for years. Yet, over the last decade, many changes have been set into motion, including data pooling. This shift towards increased availability and reliability of data opens new perspectives for research, especially regarding the conception of decision support systems. This article proposes one application of complex networks in the context of humanitarian aid, using existing information. The objective is to increase the global understanding of the overall relief response, taking into account many heterogeneous elements, from existing infrastructures and local resources of the country to relief activities proposed by organisations. To take this heterogeneity into account, we used the multilayer network framework. Our approach can be used before the occurrence of disasters, to understand the weaknesses of countries’ actual situations. It can also be used during operations, either as a static cartography of current operations, or as a means to capture the dynamics of the system. It is currently limited by the lack of standardized databases, which increases the time needed to gather all the necessary information. Nevertheless, the trend is clearly going towards more professionalisation of humanitarian organisations, especially regarding data management. Our proposal fits with the current needs of humanitarian practitioners, to understand better the environment in which they operate.

MSSA: Constant Time State Search through Multi-Scope State Area

In the stateful data plane, the switch can record the state and forward packets based on the local state. This approach makes it possible to integrate complex network applications into the data plane, thus reducing the amount of communication required between the switch and the controller. However, due to the time it takes to look up the state for packets, packet-forwarding latency has increased. With increased network traffic, a large number of states may be recorded in the switch, and the problem of increased packet-forwarding latency caused by the lookup state becomes more serious. In this paper, we propose the multi-scope state area (MSSA) for recording state inside the switch, which can achieve a fixed-time state lookup in a large-scale state. MSSA divides the state sharing scope by associating with the switch’s multiple match–action tables, and the shared scope is used to determine the state area for recording state. When processing a packet, the state required will only be in a limited number of states that are recorded in a few state areas. We implemented a prototype pipeline that supports MSSA based on Intel’s DPDK framework and investigated the effect of state type, number, location, and comparison method on state search/insertion time. The results show that the cost of MSSA search state is constant, regardless of the number of states, and MSSA has a high space utilization rate.

Investigation of stock price network based on time series analysis and complex network

Complex network is now widely used in a series of disciplines such as biology, physics, mathematics, sociology and so on. In this paper, we construct the stock price trend network based on the knowledge of complex network, and then propose a method based on information entropy to divide the stock network into some communities, that is, a gathering study of stock price trend. We construct time series networks for each stock in Chinese A-share market based on time series network model, and then use these networks to divide the stock market into communities. We find that the average trend of stocks in the same community is the same as the trend of market value weighting, but the average trend of stocks in different communities is quite different and the sequence correlation is low. This conclusion shows that stocks in the same community share the same price trend, while the stock trend in different communities varies. This paper is a successful application of complex network and information entropy in stock trend analysis, which mainly includes two contributions. First, the success of the visibility graph algorithm provides a new perspective for enriching stock price trend modeling. Second, our conclusion proves that the clustering based on information entropy theory is effective, which provides a new method for further research on stock price trend, portfolio construction and stock return prediction.