Complex Network Model Research Articles

SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction.

Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.

Synchronization of Turing patterns in complex networks of reaction–diffusion systems set in distinct domains

We present an innovative complex network of reaction–diffusion systems set in distinct domains, with boundary couplings. The complex network models the evolution of interacting populations living in a heterogeneous and fragmented habitat, whose biological individuals migrate from one patch to another. In our model, the displacements of individuals are described by mixed boundary couplings, involving both the Neumann and Robin boundary conditions, which improve the modeling of migrations by point-wise couplings. We investigate the cases of diffusion in isotropic and anisotropic habitats and establish original sufficient conditions of synchronization in this complex network model, for complete graphs, cyclic graphs and rings of nearest neighbors. In parallel, we apply our theoretical framework to a nonlinear predator–prey model with Leslie–Gower-type functional response and explore numerically the emergence of synchronization on heterogeneous Turing patterns.

Primary driving factors of ecological environment system change based on directed weighted network illustrating with the Three-River Headwaters Region

The primary driving factors of ecological environment change have received significant attention. However, previous research methods for identifying the main drivers of ecological environment change have primarily relied on correlation analysis and regression analysis. While these methods can reveal co-occurrences, associations, and correlations among elemental characteristics, they often struggle to uncover the deep-seated interactions among elements within complex, unstable, nonlinear, and high-dimensional systems. To address this, we used the Three-River Headwaters Region as a case study and introduced a complex network model from the perspective of the ecological environment system to investigate the main driving factors of ecological environment change. In our analysis, we considered 12 factors related to the atmosphere, hydrology, vegetation, and soil, including evaporation, long-wave radiation, short-wave radiation, specific humidity, soil temperature, precipitation rate, soil water content, air temperature, air pressure, vegetation normalization index, wind speed, and natural surface runoff. Watersheds were selected as the fundamental units for constructing ecological environment datasets. We applied the Ensemble Empirical Mode Decomposition (EEMD) method and Hilbert-Huang Transform (HHT) to analyze causal relationships between time series pairs and constructed two directed weighted network models based on sub-catchments. The results showed that both network models yielded consistent conclusions, with the sparse network exhibiting higher efficiency. Radiation and temperature were identified as the primary driving factors of ecosystem change, and the water cycle was determined to be the ultimate manifestation of ecological system change throughout the Three-River Headwaters Region. Furthermore, based on node out-strength, we generated a vegetation protection priority map.

Turing instability analysis of a reaction–diffusion system for rumor propagation in continuous space and complex networks

Research on the propagation of online rumors necessitates careful consideration of rumor-spreading mechanisms. Media correction, self-correction, and the time delays associated with contact propagation are expressly introduced into the established reactive–diffusion rumor-spreading model in this paper. The conditions for a unique positive equilibrium point in the model are analyzed, laying the necessary groundwork for the occurrence of Turing instability in reaction–diffusion rumor propagation systems. A linearization approach is employed to derive the conditions for Turing instability in scenarios involving small delay to address the challenges posed by models with time delay. Furthermore, in order to anticipate the Turing pattern formation in advance, we employed a multiscale analysis approach to derive the amplitude equation within this model. The validity of the theoretical results is verified through numerical simulations, while the propagation dynamics of the model were analyzed within two complex network models, namely small-world networks and scale-free networks. Simulation results indicate that increasing time delay decelerates rumor propagation, while higher degrees of cross-diffusion, contact spreading, media correction, and self-correction expedite the spread of rumors. In small-world networks, the trust level in rumors has a narrower range, while in scale-free networks, the stability of rumors is faster. This indicates how to effectively adjust network structures for controlling or managing the spread of rumors.

Evaluating Losses from Water Scarcity and Benefits of Water Conservation Measures to Intercity Supply Chains in China.

The severe water scarcity in China poses significant economic risks to its agriculture, energy, and manufacturing sectors, which can have a cascading effect through the supply chains. Current research has assessed water scarcity losses for global countries and Chinese provinces by using the water scarcity risk (WSR) method. However, this method involves subjective functions and parameter settings, and it fails to capture the adaptive behaviors of economies to water scarcity, compromising the reliability of quantified water scarcity loss. There is a pressing need for a new method to assess losses related to water scarcity. Here, we develop an agent-based complex network model to estimate the inter-regional and intersectoral impacts of water scarcity on both cities and basins. Subsequently, we evaluate the supply chain-wide economic benefits of four different water conservation measures as stipulated by the 14th Five-Year Plan for the Construction of a Water-Saving Society. These measures include increasing the utilization rate of recycled water in water-scarce cities, reducing the national water consumption per industrial value-added, and implementing agricultural and residential water conservation measures. Results show that direct losses constitute only 9% of the total losses from water scarcity. Approximately 37% of the losses can be attributed to interregional impacts. Among the water-scarce cities, Qingdao, Lanzhou, Jinan, and Zhengzhou pose a significant threat to China's supply chains. Agricultural water conservation yields the highest amount of water savings and economic benefits, while residential water conservation provides the highest economic benefit per unit of water saved. The results provide insights into managing water scarcity, promoting cross-regional cooperation, and mitigating economic impacts.

A Method for Identifying Key Nodes Based on Gravity Model in Complex Networks

A Method for Identifying Key Nodes Based on Gravity Model in Complex Networks

Research on the Synchronization Control of Fractional-Order Complex Networks Based on Switching Topology

Abstract: In the contemporary epoch, bolstered by information technology, the quintessence of networks is ubiquitously manifested, with a plethora of network types—ranging from the Internet, vehicular traffic frameworks, electrical distribution systems, cellular communication matrices, to social interconnection webs—being intricately woven into the fabric of societal functionality and quotidian existence. The domain of complex networks has burgeoned into a fervently pursued research vector, magnetizing an eclectic cohort of investigators from disciplines as variegated as mathematics, biosciences, and engineering. Notably, the fractional calculus has eclipsed its integer-order counterpart by offering enhanced precision in the depiction of real-world systems and phenomena. Consequently, the infusion of fractional calculus into the modeling of complex networks, to dissect their dynamic attributes and attendant control paradigms, has crystallized as a research nexus of burgeoning interest, eliciting scholarly discourse at both national and international echelons. [Purpose] This inquiry into the synchronization control of fractional-order complex networks, predicated on switching topology, endeavors to harness said topology as a scaffold for probing the synchronicity inherent within fractional-order complex networks. The objective is to augment the operational efficacy of these networks, broaden their sphere of applicability, and fortify the synchronal linkage amongst fractional-order complex networks and their counterparts. [Method] Predominantly, this exploration is underpinned by a synthesis of bibliographic scrutiny and analytical modeling, employing an extensive compendium of model equations to elucidate the subject matter. The spotlight is cast upon the Caputo fractional-order differential equation, with a focus on assaying the stability traits of its equilibrium junctures and formulating more expansive and pragmatic conditions for stability. In addition, to facilitate the precise estimation of elusive topologies within complex networks, a supplementary network—comprising isolated nodes and a regulatory protocol—is conceptualized. [Results] The findings suggest that the investigation into synchronization control based on switching topology propels the advancement of fractional-order complex networks and has substantial reference value. It serves to substantiate, to a certain degree, the postulations of antecedent theorists and chart a trajectory for ensuing scholarly endeavors in cognate domains, thereby perpetually amplifying the pragmatic utility of fractional-order complex networks.

Stability of Regional Ecological Supply–Demand Is Enhanced by Complex Network Modeling: Evidence From the Xuzhou Metropolitan Area, China

Stability of Regional Ecological Supply–Demand Is Enhanced by Complex Network Modeling: Evidence From the Xuzhou Metropolitan Area, China

NoC simulation steered by NEST: McAERsim and a Noxim patch.

Great knowledge was gained about the computational substrate of the brain, but the way in which components and entities interact to perform information processing still remains a secret. Complex and large-scale network models have been developed to unveil processes at the ensemble level taking place over a large range of timescales. They challenge any kind of simulation platform, so that efficient implementations need to be developed that gain from focusing on a set of relevant models. With increasing network sizes imposed by these models, low latency inter-node communication becomes a critical aspect. This situation is even accentuated, if slow processes like learning should be covered, that require faster than real-time simulation. Therefore, this article presents two simulation frameworks, in which network-on-chip simulators are interfaced with the neuroscientific development environment NEST. This combination yields network traffic that is directly defined by the relevant neural network models and used to steer the network-on-chip simulations. As one of the outcomes, instructive statistics on network latencies are obtained. Since time stamps of different granularity are used by the simulators, a conversion is required that can be exploited to emulate an intended acceleration factor. By application of the frameworks to scaled versions of the cortical microcircuit model-selected because of its unique properties as well as challenging demands-performance curves, latency, and traffic distributions could be determined. The distinct characteristic of the second framework is its tree-based source-address driven multicast support, which, in connection with the torus topology, always led to the best results. Although currently biased by some inherent assumptions of the network-on-chip simulators, the results suit well to those of previous work dealing with node internals and suggesting accelerated simulations to be in reach.

Higher-order random network models

Most existing random network models that describe complex systems in nature and society are developed through connections that indicate a binary relationship between two nodes. However, real-world networks are so complicated that we can only identify many critical hidden structural properties through higher-order structures such as network motifs. Here we propose a framework in which we define higher-order stubs, higher-order degrees, and generating functions for developing higher-order complex network models. Then we develop higher-order random networks with arbitrary higher-order degree distributions. The developed higher-order random networks share critical structural properties with real-world networks, but traditional connection-based random networks fail to exhibit these structural properties. For example, as opposed to connection-based random network models, the proposed higher-order random network models can generate networks with power-law higher-order degree distributions, right-skewed degree distributions, and high average clustering coefficients simultaneously. These properties are also observed on the Internet, the Amazon product co-purchasing network, and collaboration networks. Thus, the proposed higher-order random networks are necessary supplements to traditional connection-based random networks.

Analyzing the diffusion of feminist discourses on Chinese social media: A case study of the 2022 Tangshan restaurant attack.

Network platforms have ushered in a novel propagation model for feminist discourses. The emergence of oriental feminism in society has led to gender-based public opinions surrounding public events becoming a trending topic on Chinese social media. This study uses the 2022 Tangshan restaurant attack as a case study, an incident that sparked widespread discussions across China in 2022. The research gathered 366,602 network communication nodes within a week and examined the communication networks of three types of content nodes (information, opinion, and appeasement) using the complex network modeling method. The findings revealed that all three types of information communication networks exhibit an apparent scale-free characteristic, and the "key minority" of nodes significantly affects information communication. Information-type and appeasement-type Weibo display notable similarities in the quantity and degree distribution of nodes within the communication networks and in the information decay rate. Moreover, authoritative information issuers have become the primary catalyst for information propagation. Conversely, opinion-type Weibo has the widest communication network diameter and features a high degree of participation, multilevel propagation, and a slow decay rate. This indicates that the interaction between opinion leaders and netizens has enhanced the depth and breadth of information diffusion for opinion-type Weibo.

ICD-10 - ORPHA: An Interactive Complex Network Model for Brazilian Rare Diseases

A disease is considered rare if it has a low prevalence. It is estimated that around 400 million people worldwide have a rare disease, including 15 million in Brazil. Consequently, it became a public health priority for the World Health Organization and the Brazilian Health Ministry. In 2014, the Brazilian government launched a national policy regarding the care for rare patients’, the Ordinance nº199. The national politic defines guidelines, procedures, and descriptions of rare disease codes to provide access and diagnosis in the public health system to reduce mortality and improve patient’s quality of life. Diseases are identified according to the International Classification of Diseases 10th Revision, a widely used terminology in this context. However, there are also different terminologies to codify a rare disease, such as the ORPHAcode provided by Orphanet. This paper proposes a complex network model using the terminologies’ relationship to show that the International Classification of Diseases 10th Revision may be generic for diagnosing rare Brazilian patients. Moreover, there is no perfect nomenclature to define rare diseases, but each context has a better application. So, mapping the relationship between each terminology is fundamental for creating consistent semantic relationships in biomedical ontologies, providing a functional environment for carrying out tasks involving more than one terminology.

Modeling and Analyzing Information Propagation Evolution Integrating Internal and External Influences

AbstractOnline social networks have revolutionized communication, providing individuals with platforms to express their personal opinions on diverse topics. Researchers have independently explored information propagation and opinion evolution within complex networks. However, these phenomena exhibit interconnectedness, where information dissemination influences opinion evolution and vice versa. To address challenges in complex network modeling and opinion‐information coupling, internal and external factors are considered in public opinion scenarios by incorporating the crowd effect, enhancement effect, and evolutionary game theory. The susceptible‐latent‐forwarding‐immune‐Jager‐Amblard (SLFI‐JA) model is presented by modifying the SLFI propagation dynamics model and the JA opinion dynamics model, enabling the integration of information propagation and opinion evolution at the microlevel. Through analyzing real‐world social hotspots on Sina Weibo, case studies and comparative analyses are conducted to validate the rationality and effectiveness of the proposed model. Furthermore, the findings identify key factors influencing public opinion dissemination and group opinion evolution, offering valuable insights to relevant departments in public opinion response and management. The study aims to mitigate the harmful effects of negative public opinions, prevent extreme adverse online events, and foster a healthier online environment.

Fault Detection and Isolation in Smart-Grid Networks of Intelligent Power Routers Modeled as Probabilistic Boolean Networks

A self-organizing complex-network modeling method, probabilistic Boolean networks, is presented as a model-based diagnostic system for detecting and isolating different types of faults, failures, and modes of operation in which a network of intelligent power routers is deployed over a standard power test case: the Western System Coordinating Council 9 Bus System. Such a system allows designers and engineering professionals to make educated decisions pertaining to the design of smart-grid systems endowed with intelligent power routers. There is a recurrent necessity to design reliable and fault-tolerant smart power systems, maintaining adequate operation and adherence to performance specifications, while keeping costs at the minimum. This diagnostics system will help achieve such goals: better design through thorough analysis of the conditions that lead to a fault on a smart grid, proper detection of these faults, and isolation of the respective assets.

Dynamic analysis and influence mechanism of digital technology diffusion in the energy industry based on the evolutionary game model of complex networks

Dynamic analysis and influence mechanism of digital technology diffusion in the energy industry based on the evolutionary game model of complex networks

Machine learning-based prediction of Q-voter model in complex networks

In this article, we consider machine learning algorithms to accurately predict two variables associated with the Q-voter model in complex networks, i.e. (i) the consensus time and (ii) the frequency of opinion changes. Leveraging nine topological measures of the underlying networks, we verify that the clustering coefficient (C) and information centrality emerge as the most important predictors for these outcomes. Notably, the machine learning algorithms demonstrate accuracy across three distinct initialization methods of the Q-voter model, including random selection and the involvement of high- and low-degree agents with positive opinions. By unraveling the intricate interplay between network structure and dynamics, this research sheds light on the underlying mechanisms responsible for polarization effects and other dynamic patterns in social systems. Adopting a holistic approach that comprehends the complexity of network systems, this study offers insights into the intricate dynamics associated with polarization effects and paves the way for investigating the structure and dynamics of complex systems through modern methods of machine learning.

Study on the characteristics and synergistic effects of industrial complex networks – empirical evidence from Chinese manufacturing

PurposeThe manufacturing industry and the producer service industry have a high degree of industrial correlation, and their integration will cause changes in the complex industrial network topology, which is an important reason for the synergistic effect. This paper describes the topology of industrial systems using complex network theory; further, it discusses how to identify the criticality and importance of industrial nodes, and whether node characteristics cause synergistic effects.Design/methodology/approachBased on the input-output data of China in 2007, 2012 and 2017, this paper constructs the industrial complex network of 30 Chinese provinces and cities, and measures the regional network characteristics of the manufacturing industry. The fixed-effect panel regression model is adopted to test the influence of agglomeration degree and centrality on synergies, and its adjustment mechanism is explored.FindingsThe degree of network agglomeration in the manufacturing industry exerts a negative impact on the synergistic effect, while the centrality of the network exerts a significant promoting effect on the synergistic effect. The results of adjustment mechanism test show that enhancing the autonomous controllable ability of the regional industrial chain in the manufacturing industry can effectively reduce the effect of network characteristics on the synergistic effect.Research limitations/implicationsBased on input-output technology, this paper constructs a complex industrial network model, however, only basic flow data are used. Considerable in-depth and detailed research on the economic and technological connections within the industry should be conducted in the future. The selection of the evaluation index of the importance of industrial nodes also needs to be further considered. For historical reasons, it is also difficult to obtain and process data when carrying out quantitative analysis; therefore, it is necessary to make further attempts from the data source and the expression form of evaluation indicators.Practical implicationsIn a practical sense this has certain reference value for the formulation of manufacturing industrial policies the optimization of regional industrial layout and the improvement of the industrial development level. It is necessary to formulate targeted and specialized industrial development strategies according to the characteristics of the manufacturing industry appropriately regulate the autonomous controllable ability of the industrial chain and avoid to limit the development of industries which is in turn limited by regional resources. Industry competition and market congestion need to be reduced industry exchanges outside the region encouraged the industrial layout optimized and the construction of a modern industrial system accelerated.Social implicationsThe above research results hold certain reference importance for policy formulation related to the manufacturing industry, regional industrial layout optimization and industrial development level improvement. Targeted specialized industrial development strategies need to be formulated according to the characteristics of the manufacturing industry; the autonomous controllability of the industrial chain needs to be appropriately regulated; limitation of regional resources needs to be avoided as this restricts industrial development; and industry competition and market congestion need to be reduced. Agglomeration of production factors and optimization of resource allocation is an important part of a beneficial regional economic development strategy, and it is also an inevitable choice for industrialization to develop to a certain stage under the condition of a market economy. In alignment with the research conclusions, effective suggestions can be put forward for the current major industrial policies. In the process of promoting the development of the manufacturing industry, it is necessary for regional governments to carry out unified planning and guidance on the spatial layout of each manufacturing subsector. Regional governments need to effectively allocate inter-industry resources, better share economies of scale, constantly enhance the competitive advantages and competitiveness of development zones and new districts and promote the coordinated agglomeration and development of related industries with input industries. Industrial exchanges outside the region should be encouraged, the industrial layout should be optimized and the construction of a modern industrial system should be accelerated.Originality/valueComplex network theory is introduced to study the industrial synergy effect. A complex industrial network of China's 30 regions is built and key network nodes are measured. Based on the dimensionality of the “industrial node – industrial chain – industrial complex network”, the research path of industrial complex networks is improved.

A lightweight high-resolution algorithm based on deep learning for layer-wise defect detection in laser powder bed fusion

The quality of the powder bed is critical in the laser powder bed fusion (LPBF) process, and defects in the powder bed likely affect the quality of the final part. With the development of artificial intelligence technology, machine learning methods have been widely applied in powder-bed defect detection. However, to achieve high-precision defect detection, it is often necessary to construct complex network models and use high-resolution powder bed image data. To address these issues, this study used an off-axis industrial camera to capture layer-wise powder bed image data and proposed a defect detection model based on YOLOv7x and channel pruning to achieve defect identification and localization of powder bed patch images. Furthermore, an end-to-end defect detection pipeline based on image processing methods was proposed to detect defects in layer-wise powder bed images. Finally, the gradient-based class activation map technique (Grad CAM++) was used to analyze the interpretability of the detection results of the model. The results indicated that the proposed model was more lightweight than other models (YOLOv7x, Faster R-CNN, and SSD), with a model size of only 12.4MB. The average time for detecting powder bed image patches was significantly reduced to only 3.4 ms, and the average detection accuracy was as high as 97.4%. This demonstrates that the proposed detection method has the advantages of faster detection speed, higher detection accuracy, and simpler models, providing a reference for the real-time online detection of powder bed defects.

Volatility forecasting for stock market index based on complex network and hybrid deep learning model

AbstractThe existing literature on the volatility forecasting less considered the co‐movement among stock markets from the spatial dimension. This paper builds the hybrid convolutional neural networks (CNNs)–gated recurrent unit (GRU) model for volatility forecasting under high frequency financial data based on transaction information and the topological characteristics constructed through the complex network of multi‐market symbol patterns. The hybrid neural network CNN‐GRU combines the advantages of CNN automatically extracting features for the input indicators and GRU processing long and short‐term serially dependent features, which can better improve the forecasting accuracy. The empirical results show that with the integration of topological characteristics as the indicators based on complex network, the deep learning model has a significant improvement of one‐step and multi‐step volatility forecasting accuracy for the China's and the US stock markets. The research in this paper provides a complete index system from the spatial dimension and a more accurate and robust volatility forecasting method under the high‐frequency financial data.

A Copula network deconvolution-based direct correlation disentangling framework for explainable fault detection in semiconductor wafer fabrication

Wafer fabrication is a highly complex manufacturing system. Using complex network models to portray the correlation between parameters is an effective tool for finding the key influencing factors associated with the cause of wafer defects. However, the complex network in a wafer fabrication system has many nonlinearities, non-normal distributions, coupling, and spurious correlations. Quantitative clarification of direct and indirect correlations in the complex network is the prerequisite for achieving explainable fault detection in smart manufacturing. Therefore, a Copula network deconvolution-based direct correlation disentangling framework is proposed in the context of explainable fault detection in semiconductor wafer fabrication. Firstly, The complex network correlation diagram of the parameters in the wafer fabrication process is constructed with each parameter as a node and the correlation coefficients between parameters as connected edges. Then, a nonlinear correlation metric model based on adaptive Copula function selection is designed to deal with the nonlinear correlation of complex network models. The adaptive selection of the Copula function is realized by the goodness-of-fit test method based on Euclidean distance. After that, considering the coupling and spurious correlation in the complex network model, a network deconvolution-based fault detection method is designed to identify direct correlation. Finally, a case study using the actual data from semiconductor wafer fabrication systems is conducted to compare the existing mainstream fault detection methods and validate the effectiveness of the proposed method.