Small-world Network Structure Research Articles

빅데이터를 활용한 소셜미디어 기후변화 회의론 네트워크와 인플루언서 메시지 구조 분석: 사회연결망 분석과 의미연결망 분석의 적용

This study examines the information structure, message dissemination patterns, and influencer characteristics of social media users who spread climate change skepticism messages using social and semantic network analysis based on big data. Compared to a random network model, the climate skepticism network exhibits a small-world network structure, with messages led by climate change deniers spreading easily and quickly. The skepticism network is formed by a few large communities and a majority of smaller ones, with users tending to communicate strongly within their communities. The authority group in the network frequently raised conspiracy theories related to climate issues and highlighted farmer protests against Europe's Green Deal and carbon neutrality policies. The network's broadcaster group, on the other hand, tended to mock or criticize climate policy advocates in a harsh tone, often denying scientific evidence. The results can be used to inform the development of climate campaigns.

Investigating the interaction between EEG and fNIRS: A multimodal network analysis of brain connectivity

The brain is a complex system with functional and structural networks. Different neuroimaging methods have been developed to explore these networks, but each method has its own unique strengths and limitations, depending on the signals they measure. Combining techniques like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has gained interest, but understanding how the information derived from these modalities is related to each other remains an exciting open question. The multilayer network model has emerged as a promising approach to integrate different sources data. In this study, we investigated the hemodynamic and electrophysiological data captured by fNIRS and EEG to compare brain network topologies derived from each modality, examining how these topologies vary between resting state (RS) and task-related conditions. Additionally, we adopted the multilayer network model to integrate EEG and fNIRS data and evaluate the benefits of combining multiple modalities compared to using a single modality in capturing characteristic brain functioning.A small-world network structure was observed in the rest, right motor imagery, and left motor imagery tasks in both modalities. We found that EEG captures faster changes in neural activity, thus providing a more precise estimation of the timing of information transfer between brain regions in RS. fNIRS provides insights into the slower hemodynamic responses associated with longer-lasting and sustained neural processes in cognitive tasks. The multilayer approach outperformed unimodal analyses, offering a richer understanding of brain function. Complementarity between EEG and fNIRS was observed, particularly during tasks, as well as a certain level of redundancy and complementarity between the multimodal and the unimodal approach, which depends on the modality and the specific brain state. Overall, the results highlight differences in how EEG and fNIRS capture brain network topology in RS and tasks and emphasize the value of integrating multiple modalities for a comprehensive view of brain connectivity and function.

Modeling Neurodegeneration and Regeneration in Parkinson's Disease

The diagnosis and prevention of neurodegenerative diseases is a heavily examined topic in the neuroscience discipline. Whether it be from the anatomical and biological perspectives or the psychological and sociological perspectives, the ultimate goal is to discover strategies to pinpoint the infection as soon as possible. This article begins with reviewing the small-world network structure and then combines the sociological and anatomical perspectives to explain the progression of neuronal death within the brain by using rsfMRI data and the Hegselmann-Krause Model of Opinion Dynamics to illustrate critical interactions between brain regions, and to predict the ultimate behavior of the neural network after initial degeneration Following experimentation–in which critical regions related to Parkinson’s Disease were studied–thresholds were identified in specific regions which exhibited consistent converging behavior of the neural network toward either degenerative or regenerative directions. Furthermore, a simple graphical model is proposed to demonstrate the ranges of values in which current brain health could be of concern. We concluded that neuron death in one brain region can lead to further infection in the resulting system; however, some regions can also directly/indirectly compensate for the system’s decreased function. In future research endeavors, this could provide insight into developing more accurate predictive models for the goal of early detection of a diseased brain and its recovery.

A hybrid neural network based on the small-world network for inner speech recognition

This study is devoted to the identification of human inner speech using an electroencephalogram (EEG), where inner speech refers to an individual's subjective experience of language, disconnected from discernible audible articulation. The core aim of this system is the rapid and precise classification of signals via human inner speech, thus facilitating enhanced control and interaction functionalities. The research entails a comprehensive analysis of 10 volunteers' brain activity across 128 channels from OpenNeuro's Inner speech dataset. A hybrid neural network, which incorporates the small-world network structure, is employed to model neural activity within the brain. This approach outperforms random chance and aligns with current research expectations.

An evolutionary mechanism of social preference for knowledge sharing in crowdsourcing communities

Crowdsourcing community, as an important way for enterprises to obtain external public innovative knowledge in the era of the Internet and the rise of users, has a very broad application prospect and research value. However, the influence of social preference is seldom considered in the promotion of knowledge sharing in crowdsourcing communities. Therefore, on the basis of complex network evolutionary game theory and social preference theory, an evolutionary game model of knowledge sharing among crowdsourcing community users based on the characteristics of small world network structure is constructed. Through Matlab programming, the evolution and dynamic equilibrium of knowledge sharing among crowdsourcing community users on this network structure are simulated, and the experimental results without considering social preference and social preference are compared and analysed, and it is found that social preference can significantly promote the evolution of knowledge sharing in crowdsourcing communities. This research expands the research scope of the combination and application of complex network games and other disciplines, enriches the theoretical perspective of knowledge sharing research in crowdsourcing communities, and has a strong guiding significance for promoting knowledge sharing in crowdsourcing communities.

Platform collaboration patterns, network structure, and innovation performance: An NK simulation approach

Prior research lacks understanding about how collaborative arrangements and network structures influence innovation outcomes in platform-based ecosystems. This paper addresses this gap by using an NK simulation model to investigate the impacts of different collaboration patterns and network structures on innovation performance. The NK simulation approach overcomes the shortcomings of empirical methods and enables examining the dynamic impacts. The results reveal that the “special platform + generic complementor” pattern leads to the highest innovation output. The impact of component correlations on the innovation performance follows an inverted U-shape. The small world network structure promotes innovation versus regular or random networks. The results provide novel theoretical insights into strategically configuring platform partnerships and network connections to optimize innovation. The findings offer practical guidance for firms to choose beneficial collaboration pattern and design proper network structure.

New Short Scale to Measure Workers’ Attitudes Toward the Implementation of Cooperative Robots in Industrial Work Settings: Instrument Development and Exploration of Attitude Structure

The implementation of new robotic technology at workplaces is oftentimes accompanied by social and organizational change processes. A new context-specific questionnaire was developed as a diagnostic tool to measure workers’ attitudes toward mobile cooperative manufacturing robots to provide a basis for managerial decisions and interventions—the “Attitudes toward Cooperative Industrial Robots Questionnaire” (ACIR-Q). Two samples, an online sample of 355 German manufacturing workers and a field sample of 201 workers from 4 local manufacturing companies were collected. For a large item pool, exploratory and confirmatory factor analysis was used to identify the attitudinal factor structure. Data showed a combined affective-behavioral factor and two cognitive factors on task-related and social-related beliefs. Based on this, the 12-item short scale ACIR-Q was derived using ant colony optimization. As attitudes can also be interpreted as networks of evaluative responses, network analysis was used for further insights. The small-world network structure (high clustering and connectivity) allows to hold complex attitudes and centrality measures indicate the most influential evaluative responses. Additionally, we explored relationships between workers’ attitudes and interpersonal variables (perceived competence, perceived control, and general self-efficacy), as well as social/organizational variables (trust in management, support climate, job insecurity and job characteristics). Based on the results, practical implications are suggested to improve workers’ attitudes.

Optimal Resource Allocation for 5G Network Slice Requests Based on Combined PROMETHEE-II and SLE Strategy.

The network slicing of physical infrastructure is required for fifth-generation mobile networks to make significant changes in how service providers deliver and defend services in the face of evolving end-user performance requirements. To perform this, a fast and secure slicing technique is employed for node allocation and connection establishment, which necessitates the usage of a large number of domain applications across the network. PROMETHEE-II and SLE algorithms were used in this study's approach to network design for node allocation and link construction, respectively. The PROMETHEE-II approach takes into account a variety of node characteristics while constructing a node importance rank array (NIRA), including the node capacity, bandwidth of neighboring connections, degree of the node, and proximity centrality among others. The SLE method is proposed to record all possible link configurations for the network slice request (NSR) nodes to guarantee that the shortest path array (SPA) of the NSR has a high acceptance rate. Performance metrics such as the service revenue and acceptance ratio were considered to evaluate the effectiveness of the suggested approach. The effectiveness of network slicing has been further examined under different infrastructure models to determine whether a small-world network structure is beneficial to 5G network. For each scenario, simulations were carried out and the results were compared to previously published findings from other sources.

Network analysis reveals context-dependent structural complexity of social calls in serrate-legged small treefrogs.

Vocal communication plays an important role in survival, reproduction, and animal social association. Birds and mammals produce complex vocal sequence to convey context-dependent information. Vocalizations are conspicuous features of the behavior of most anuran species (frogs and toads), and males usually alter their calling strategies according to ecological context to improve the attractiveness/competitiveness. However, very few studies have focused on the variation of vocal sequence in anurans. In the present study, we used both conventional method and network analysis to investigate the context-dependent vocal repertoire, vocal sequence, and call network structure in serrate-legged small treefrogs Kurixalus odontotarsus. We found that male K. odontotarsus modified their vocal sequence by switching to different call types and increasing repertoire size in the presence of a competitive rival. Specifically, compared with before and after the playback of advertisement calls, males emitted fewer advertisement calls, but more aggressive calls, encounter calls, and compound calls during the playback period. Network analysis revealed that the mean degree, mean closeness, and mean betweenness of the call networks significantly decreased during the playback period, which resulted in lower connectivity. In addition, the increased proportion of one-way motifs and average path length also indicated that the connectivity of the call network decreased in competitive context. However, the vocal sequence of K. odontotarsus did not display a clear small-world network structure, regardless of context. Our study presents a paradigm to apply network analysis to vocal sequence in anurans and has important implications for understanding the evolution and function of sequence patterns.

Condensates formed by prion-like low-complexity domains have small-world network structures and interfaces defined by expanded conformations

Biomolecular condensates form via coupled associative and segregative phase transitions of multivalent associative macromolecules. Phase separation coupled to percolation is one example of such transitions. Here, we characterize molecular and mesoscale structural descriptions of condensates formed by intrinsically disordered prion-like low complexity domains (PLCDs). These systems conform to sticker-and-spacers architectures. Stickers are cohesive motifs that drive associative interactions through reversible crosslinking and spacers affect the cooperativity of crosslinking and overall macromolecular solubility. Our computations reproduce experimentally measured sequence-specific phase behaviors of PLCDs. Within simulated condensates, networks of reversible inter-sticker crosslinks organize PLCDs into small-world topologies. The overall dimensions of PLCDs vary with spatial location, being most expanded at and preferring to be oriented perpendicular to the interface. Our results demonstrate that even simple condensates with one type of macromolecule feature inhomogeneous spatial organizations of molecules and interfacial features that likely prime them for biochemical activity.

Shortening the distance between firms for M&A: the influence of small-world network structure on mergers and acquisitions

PurposeA small-world network is a type of network structure in which nodes are highly clustered and at short distances without being directly linked. This article analyzes whether the network of interlocking directorates among the largest Brazilian corporations follows a small-world network structure and if the small-world properties (high clustering and short distance between nodes) influence the occurrence of M&A at the domestic and international level.Design/methodology/approachThe authors tested hypotheses regarding the relationship between small-world network properties and M&A based on a sample of large publicly-listed corporations in Brazil for the time series of 2000–2015 and using network analysis and regression techniques (probit and OLS).FindingsThe results show that while the Brazilian corporate network fits the small-world features of high clustering and short path lengths, only the distance among connected firms has a significant effect on international M&A: the shorter the distance between firms, the more likely firms undertake M&A abroad. Moreover, being integrated into the main component has a significant positive effect on national and international M&A. These findings suggest that the information and knowledge to undertake M&A can be better acquired by belonging to large business communities and not local cohesive clusters.Originality/valueThis research contributes to theories and ongoing debates about the network effects on organizational decisions and the determinants of M&A in emerging markets. In addition, this is the first study to analyze the impact of small-world networks on international M&A while controlling for country-level variables.

STRUCTURAL EVOLUTION OF KNOWLEDGE NETWORK AND BEHAVIOR VARIATION OF KNOWLEDGE SUBJECT DRIVEN BY EMOTIONAL BEHAVIOR

BackgroundThe perception behavior of the knowledge subjects can get certain feedback and enlightenment from the trend variation of knowledge level. The change of the psychological expectation and behavior of the knowledge subjects is of great significance to the evolution and utilization of the knowledge network structure in the complex system. At present, there have been many mature researches on the content of knowledge flow and knowledge diffusion between organizations based on network structure. This article combines the relevant theories of micro-behavioral psychology from the perspective of complex system evolution, to further explore the influences of four typical complex network structures evolution on the knowledge transfer behavior of the knowledge subjects and the knowledge level of the subjects.Subjects and MethodsIn this paper, a simulation research process is designed based on a complex knowledge network model. The calculation indicators mainly include three aspects: network value, node coupling costs, and weight values of relational edges. The node coupling costs are composed of three dimensions: the psychological distance cost between network nodes, the difference in cooperation value between network nodes, and the difference in connectivity between network nodes. Using programming, four programs are used to simulate and analyze the evolution of complex network structure based on the coupling cost of the knowledge subjects and the psychological expectations and behavior changes of the knowledge subjects.ResultsThe results of this paper show that the more frequent the interaction between network subjects, which is not necessarily greater beneficial to the knowledge subjects. In addition, the fair distribution of knowledge resources is not conducive to the growth of knowledge level. Among the four types of typical network evolution, the development of the small-world network structure is relatively stable, and the adaptive behavior of the knowledge subjects has produced relatively high expected value.ConclusionsThe stability of the network environment has an exogenous impact on the expectation and value level of the knowledge subjects. The development environment of the evolution of the small-world network structure is relatively stable, which can generate high psychological expectations for the adaptive behavior of the knowledge subjects. It provides a reference value for the knowledge transfer of the knowledge subjects in the evolution of the knowledge network. This article discusses the behavioral changes of network subjects from the perspective of the psychology of knowledge subjects, which has certain practical significance. However, it does not consider the initial endowment differences between the nodes and the alternate evolution process between complex networks. Therefore, follow-up researches can further optimize the understanding on the differences between subjects in the complex network and analyze the adaptive behavior of knowledge subjects.AcknowledgementsSupported by projects grant from National Natural Science Foundation of China (Grant No. 71602012); Humanities and Social Sciences Fund of the Ministry of Education (Grant No. 21YJC790122); and Philosophy and Social Science Fund Project of CDUT (Grant No. YJ2020-JX002).

Analysis and Optimization of Network Properties for Bionic Topology Hopfield Neural Network Using Gaussian-Distributed Small-World Rewiring Method

The fully connected topology, which coordinates the connection of each neuron with all other neurons, remains the most commonly used structure in Hopfield-type neural networks. However, fully connected neurons may form a highly complex network, resulting in a high training cost and making the network biologically unrealistic. Biologists have observed a small-world topology with sparse connections in the actual brain cortex. The bionic small-world neural network structure has inspired various application scenarios. However, in previous studies, the long-range wirings in the small-world network have been found to cause network instability. In this study, we investigate the influence of neural network training on the small-world topology. The role of the path length and clustering coefficient of neurons is expounded in the neural network training process. We employ Watt and Strogatz’s small-world model as the topology for the Hopfield neural network and conduct computer simulations. We observe that the random existence of neuron connections may cause unstable network energies and generate oscillations during the training process. A new method is proposed to mitigate the instability of small-world networks. The proposed method starts with a neuron as the pattern centroid along the radial, which arranges its wirings in compliance with the Gaussian distribution. The new method is tested on the MNIST handwritten digit dataset. The simulation confirms that the new small-world series has higher stability in terms of the learning accuracy and a higher convergence speed compared with Watt and Strogatz’s small-world model.

Network structure, portfolio diversification and systemic risk

We investigate the effect of portfolio diversification on banking systemic risk, where the network effect is incorporated. We analyze three kinds of interbank networks, namely, random networks, small-world networks and scale-free networks. We show that the effect of portfolio diversification on banking systemic risk depends on interbank network structures and shock types. First, systemic risk increases first and then reduces with the increase of the level of portfolio diversification in the case of the individual shock. Second, in the case of the systemic shock, systemic risk reduces with the increases of the level of portfolio diversification. Third, banking systems with scale-free network structures are the most stable, and those with small-world network structures are the most vulnerable.

Small-world network structure mimics spontaneous synchronization in epileptic seizures

Model using complex coupled oscillator networks show the impact of neural network structure in seizure-related synchronization.

Social culture and innovation diffusion: a theoretically founded agent-based model

This study proposes an agent-based model to theoretically investigate the effects of social culture on innovation diffusion. The model assumes that social culture (i.e., individualism, power distance, and uncertainty avoidance from Hofstede’s cultural dimension theory) has a direct effect on the small-world network structure and individual characteristics. We further explore how the characteristics of innovation influence the diffusion process. We find that individualism has a positive effect on the diffusion speed in the early stage, whereas uncertainty avoidance and power distance have negative effects on innovation diffusion. The effect of uncertainty avoidance on the diffusion speed turns positive after the early stage of diffusion and the negative effect of power distance becomes positive in the late stage. We compare real-world diffusion data with the proposed agent-based model, finding some similarities in the diffusion patterns. The characteristics of innovation affect innovation diffusion when the uncertainty avoidance is high. However, when both uncertainty avoidance and individualism are low, the effect of the characteristics of an innovation on diffusion is restricted.

Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song.

Vocal communication systems have a set of rules that govern the arrangement of acoustic signals, broadly defined as 'syntax'. However, there is a limited understanding of potentially shared or analogous rules across vocal displays in different taxa. Recent work on songbirds has investigated syntax using network-based modelling. This technique quantifies features such as connectivity (adjacent signals in a sequence) and recurring patterns. Here, we apply network-based modelling to the complex, hierarchically structured songs of humpback whales (Megaptera novaeangliae) from east Australia. Given the song's annual evolving pattern and the cultural conformity of males within a population, network modelling captured the patterns of multiple song types over 13 consecutive years. Song arrangements in each year displayed clear 'small-world' network structure, characterized by clusters of highly connected sounds. Transitions between these connected sounds further suggested a combination of both structural stability and variability. Small-world network structure within humpback songs may facilitate the characteristic and persistent vocal learning observed. Similar small-world structures and transition patterns are found in several birdsong displays, indicating common syntactic patterns among vocal learning in multiple taxa. Understanding the syntactic rules governing vocal displays in multiple, independently evolving lineages may indicate what rules or structural features are important to the evolution of complex communication, including human language.

Adaptive rewiring in weighted networks

Human connectome studies suggest that the brain has a modular small world network structure with rich-club effect. Such structure emerges spontaneously in simple model neural networks, (e.g. coupled maps), through adaptive rewiring according to the dynamic functional connectivity. The utility of adaptive rewiring has so far exclusively been demonstrated for unweighted networks; it is anything but guaranteed to work as well for weighted networks. We investigate adaptive rewiring in weighted networks, comparing various right-skewed, symmetrical, and left-skewed fixed weight distributions. We examine how network clustering, path length, modularity, and rich club coefficients develop for weakly, intermediate and strongly coupled networks. At low coupling strength, the weight distribution, as well as episodes of functional synchrony, have a significant effect on network evolution. With increased coupling strengths, all weighted networks robustly develop architectures similar to the unweighted ones. Adaptive rewiring appears relatively ineffective in networks with (biologically implausibly) extreme right-skewed weight distributions but performed most economically in biologically plausible log-normal distributions.

Modeling the Dynamics of Cascading Failures in Power Systems

In this paper, we use a circuit-based power flow model to study the cascading failure propagation process, and combine it with a stochastic model to describe the uncertain failure time instants, producing a model that gives a complete dynamic profile of the cascading failure propagation beginning from a dysfunctioned component and developing eventually to a large-scale blackout. The sequence of failures is determined by voltage and current stresses of individual elements, which are governed by deterministic circuit equations, while the time durations between failures are described by stochastic processes. The use of stochastic processes here addresses the uncertainties in individual components’ physical failure mechanisms, which may depend on manufacturing quality and environmental factors. The element failure rate is related to the extent of overloading. A network-based stochastic model is developed to study the failure propagation dynamics of the entire power network. Simulation results show that our model generates dynamic profiles of cascading failures that contain all salient features displayed in historical blackout data. The proposed model thus offers predictive information about occurrences of large-scale blackouts. We further plot cumulative distribution of the blackout size to assess the overall system’s robustness. We show that heavier loads increase the likelihood of large blackouts and that small-world network structure would make cascading failure propagate more widely and rapidly than a regular network structure.

Chinese Writing of Deaf or Hard-of-Hearing Students and Normal-Hearing Peers from Complex Network Approach.

Deaf or hard-of-hearing individuals usually face a greater challenge to learn to write than their normal-hearing counterparts. Due to the limitations of traditional research methods focusing on microscopic linguistic features, a holistic characterization of the writing linguistic features of these language users is lacking. This study attempts to fill this gap by adopting the methodology of linguistic complex networks. Two syntactic dependency networks are built in order to compare the macroscopic linguistic features of deaf or hard-of-hearing students and those of their normal-hearing peers. One is transformed from a treebank of writing produced by Chinese deaf or hard-of-hearing students, and the other from a treebank of writing produced by their Chinese normal-hearing counterparts. Two major findings are obtained through comparison of the statistical features of the two networks. On the one hand, both linguistic networks display small-world and scale-free network structures, but the network of the normal-hearing students' exhibits a more power-law-like degree distribution. Relevant network measures show significant differences between the two linguistic networks. On the other hand, deaf or hard-of-hearing students tend to have a lower language proficiency level in both syntactic and lexical aspects. The rigid use of function words and a lower vocabulary richness of the deaf or hard-of-hearing students may partially account for the observed differences.