Evolving Network Model Research Articles

Evolutionary Game Analysis of Complex Networks in Enterprise Green Technology Innovation From a Prospect Theory Perspective

ABSTRACTGreen technological innovation in enterprises is a key driving force for achieving sustainable development and industrial transformation. However, how enterprises formulate effective innovation strategies by integrating subjective factors with objective market conditions within complex industrial networks remains an area requiring further exploration. This study aims to construct a complex network evolution model based on prospect theory and examines how subjective factors, such as reference points, risk preferences, and loss aversion, influence the adoption of green technology innovation in different network environments. It further explores how network characteristics, including topology, size, and node degree, affect the diffusion of innovation. Numerical analysis results indicate that in scale‐free networks, lowering reference points for enterprise gains, reducing loss aversion, increasing risk preference, expanding network size, and raising average node degree generally promote higher adoption of green technology innovation. In small‐world networks, the dependence on reference points is relatively lower, risk preference and average node degree demonstrate more complex impacts. Additionally, a moderate rewiring probability can enhance green technology innovation adoption in small‐world networks. These findings provide new insights and practical implications for understanding the driving mechanisms of green technological innovation in enterprises. They further emphasize the importance of government interventions tailored to the specific characteristics of industrial networks to effectively facilitate the diffusion of green technological innovation.

Degree Distribution of Evolving Network with Node Preference Deletion

Discussing evolutionary network models and corresponding degree distributions under different mechanisms is applied basic research in network science. This study proposes a new evolutionary network model, which integrates node preference deletion and edge reconnection mechanisms and is also an extension of the existing evolutionary network model. In order to analyze the key statistical property of the model, the steady-state distribution, we propose a Markov chain method based on the enhanced stochastic process rule (ESPR). The ESPR method makes the evolving network’s topological structure and statistical properties consistent with those observed in the natural evolution process, ensures the theoretical results of the degree distribution of the evolving network model, and overcomes the limitations of using empirical methods for approximate analysis. Finally, we verify the accuracy of the steady-state distribution and tail feature estimation of the model through Monte Carlo simulation. This work has laid a solid theoretical foundation for the future development of evolutionary network models and the study of more complex network statistical properties.

Generating complex networks through a vertex merging mechanism: Empirical and analytical analysis

One of the most well-known mechanisms contributing to the emergence of networks with a power-law degree distribution is preferential attachment. In this study, we examined a family of network evolution models based on the merging of two arbitrary vertices, which is shown to also lead to the creation of power-law distributed networks. These models simultaneously apply rules for both node addition and merging, which reflects that many real systems exhibit the processes of growth and shrink. At each iteration, when two vertices merge, the neighbors of one of the vertices become neighbors of the other, and the vertex itself is removed from the network. In addition, at each iteration, a new vertex appears that is attached to randomly selected nodes. As an enhancement, we incorporate a triadic closure mechanism into the evolution to increase the clustering coefficient, a key characteristic of real social networks. We show that in the process of evolution any initial network converges to a stationary state with a power law degree distribution, while the number of edges, the average degree, and the average clustering coefficient saturate to a certain limit values depending on the model parameters.

Risk assessment of rainstorm disaster based on scenario construction

China, a country prone to frequent rainstorms, faces the pervasive threat of rainstorm disasters in most of its regions. This study aims to enhance the scientific understanding of the risks and impacts associated with rainstorm disasters. The focus of the research is on scenario construction, incorporating the consideration of disaster chains and utilizing various technologies to conduct a comprehensive assessment of rainstorm disaster risks. The “Scenario-Task-Capability” methodology is employed to analyze the characteristics of these disaster scenarios. In the “capability” analysis, a disaster chain is established, and an evolution network model for extremely heavy rainstorm disaster events is formulated. Taking into account the nature of risk sources, regional risk tolerance capacity, and risk control abilities, among other factors, a time series-based rainstorm disaster risk assessment model is developed. This model is validated using a case study in the southwest of China. The findings reveal that the comprehensive risks of rainstorm disasters in the study area are predominantly of medium and high levels, constituting 27.968 % and 28.101 % of the total area, respectively. These risks are concentrated in the central-west and northeast regions of the study area. Low-risk areas cover 21.381 % of the total area, primarily located in the central part of the study area and dispersed in a southwest direction. An extremely high-risk area, encompassing 22.549 % of the total area, is concentrated in the central and scattered northern regions. This study constructs a time-series risk assessment model for the rainstorm disaster chain with universal adaptability, which enriches the relevant theories of the rainstorm disaster chain evolution network; and it can also study the evolution of the direct risk and comprehensive risk of each disaster event node over time, providing a more systematic and complete assessment of the rainstorm disaster chain, as well as an analytical model and quantitative decision-making basis for different regions to assess comprehensive risks of rainstorm disasters and enhance disaster prevention and mitigation capabilities.

A Weibo local network growth model constructed from the perspective of following-followed

Abstract In order to explore the evolution process of the Weibo local network, this study first defines four factors influencing the evolution of the Weibo network. On this basis, the BA scale-free network model was enhanced by incorporating these four factors and accounting for directionality, resulting in a Weibo local network evolution model based on user attributes and behavioral similarity. The model's validity was validated by comparing simulation results with real data. The findings indicate that the Weibo local network exhibits both small-world characteristics and distinctive features. The results show that the Weibo local network exhibits both small-world characteristics and distinctive properties. The in-degree distribution follows a mixed pattern of exponential and power-law distributions, the degree-degree shows isomatching, and both the in-degree centrality and eigenvector centrality values are relatively low. This research contributes to our understanding of user behaviour in the Weibo network, and provides a structural basis for exploring the impact of Weibo network structure on information dissemination.

Reliability analysis for 5G industrial network with application and dynamic coupling

The analysis of reliability is vital in ensuring the operation of 5G industrial networks (5GIN). With 5GIN are no longer merely chains of physical components, they consist of different applications that are dynamically deployed on base platform. Such a sharing of infrastructure between applications complicates the fault logic and greatly impacts their operation. Current reliability analysis tends to treat system as static, fail to take applications and dynamic coupling into accounts. This paper proposes a model consisting of network evolution model of 5GIN (NEM5G) and reliability-based sensitivity analysis (SA). NEM5G depicts the interaction of physical components and applications, which provides a description of dynamic coupling within system and its effect on reliability. Furthermore, SA helps identify influential parameters within 5GIN to support reliability optimization. The simulation results show that, our model achieves steady-state reliability aligned with that of the Continuous Time Markov Chain (CTMC) model in a simplified intelligent optical network case. When facing complex steel processing scenario, simulation results illustrate the variation trend of 5GIN reliability, further presents an importance ranking of parameters and analyzes available design strategies.

Mapping the functional form of the trade-off between infection resistance and reproductive fitness under dysregulated immune signaling.

Immune responses benefit organismal fitness by clearing parasites but also exact costs associated with immunopathology and energetic investment. Hosts manage these costs by tightly regulating the induction of immune signaling to curtail excessive responses and restore homeostasis. Despite the theoretical importance of turning off the immune response to mitigate these costs, experimentally connecting variation in the negative regulation of immune responses to organismal fitness remains a frontier in evolutionary immunology. In this study, we used a dose-response approach to manipulate the RNAi-mediated knockdown efficiency of cactus (IκBα), a central regulator of Toll pathway signal transduction in flour beetles (Tribolium castaneum). By titrating cactus activity across four distinct levels, we derived the shape of the relationship between immune response investment and traits associated with host fitness, including infection susceptibility, lifespan, fecundity, body mass, and gut homeostasis. Cactus knock-down increased the overall magnitude of inducible immune responses and delayed their resolution in a dsRNA dose-dependent manner, promoting survival and resistance following bacterial infection. However, these benefits were counterbalanced by dsRNA dose-dependent costs to lifespan, fecundity, body mass, and gut integrity. Our results allowed us to move beyond the qualitative identification of a trade-off between immune investment and fitness to actually derive its functional form. This approach paves the way to quantitatively compare the evolution and impact of distinct regulatory elements on life-history trade-offs and fitness, filling a crucial gap in our conceptual and theoretical models of immune signaling network evolution and the maintenance of natural variation in immune systems.

Network Evolution Model with Preferential Attachment at Triadic Formation Step

It is recognized that most real systems and networks exhibit a much higher clustering with comparison to a random null model, which can be explained by a higher probability of the triad formation—a pair of nodes with a mutual neighbor have a greater possibility of having a link between them. To catch the more substantial clustering of real-world networks, the model based on the triadic closure mechanism was introduced by P. Holme and B. J. Kim in 2002. It includes a “triad formation step” in which a newly added node links both to a preferentially chosen node and to its randomly chosen neighbor, therefore forming a triad. In this study, we propose a new model of network evolution in which the triad formation mechanism is essentially changed in comparison to the model of P. Holme and B. J. Kim. In our proposed model, the second node is also chosen preferentially, i.e., the probability of its selection is proportional to its degree with respect to the sum of the degrees of the neighbors of the first selected node. The main goal of this paper is to study the properties of networks generated by this model. Using both analytical and empirical methods, we show that the networks are scale-free with power-law degree distributions, but their exponent γ is tunable which is distinguishable from the networks generated by the model of P. Holme and B. J. Kim. Moreover, we show that the degree dynamics of individual nodes are described by a power law.

Online Spreading of Topic Tags and Social Behavior

This article explores information spreading in modern online social networks. According to the law of information spread in real social networks, information retweeting is divided into two types: topic retweeting and relationship retweeting. Social behavior is considered as a higher order interaction, and the nodal influence effect in the traditional approach is abstracted as part of it for analysis. The process of topic communities being subjected to social behavior is simulated by the social behavior model, and the dynamic retweeting rate is established. A network evolution model is constructed based on the centrality and noncontinuity characteristics of topic communities in the spread process. The social reinforcement effect in information spreading is described in two dimensions by defining topic expansion rate and topic diffusion rate. This work conducts multiple views of analysis and visualization, which provide more results of quantitative aspect. The validity of the model is verified by comparing the model simulation results with real cases and the generalization ability experiments.

A new evolutional model for institutional field knowledge flow network

Abstract Purpose This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model (IKM). The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks. Design/Methodology/Approach The IKM model enhances the preferential attachment and growth observed in scale-free BA networks, while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network. To compare its performance, the BA and DMS models are also employed for simulating the network. Pearson coefficient analysis is conducted on the simulated networks generated by the IKM, BA and DMS models, as well as on the actual network. Findings The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network. It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm. The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units. Research Limitations This study has some limitations. Firstly, it primarily focuses on the evolution of knowledge flow networks within the field of physics, neglecting other fields. Additionally, the analysis is based on a specific set of data, which may limit the generalizability of the findings. Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets. Practical Implications The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions. It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations. The model can aid in optimizing knowledge flow and enhancing collaboration within organizations. Originality/value This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks. The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions. Moreover, the model has the potential to be applied to other knowledge networks, which are formed by knowledge organizations as node units.

Research on the Evolution Network Model of the Landslide Disaster Chain: A Case Study of the Baige Landslide

In the context of Western China’s unique geography, recurrent landslide disasters pose substantial threats to both resident safety and economic stability. The escalating frequency of these incidents emphasizes the critical need for innovative disaster research, particularly focused on the concept of a disaster chain. This research aims to enhance disaster preparedness and management strategies with the ultimate goal of minimizing losses. On the basis of predecessors, this study changes the previous analysis forms of single or partial disaster events, innovatively collects all secondary disaster events derived from the landslide disaster chain, and builds an evolutionary network model. In concrete terms, our study concentrates on the Baige landslide within the Qinghai-Tibet Plateau, pinpointing sub-hazard events as crucial disaster nodes within the landslide. By establishing directed connections, we have developed a comprehensive landslide disaster chain evolution network model firmly grounded in the principles of disaster chain dynamics and complex network theory. This model encompasses 31 distinct disaster nodes and 77 connecting edges. To assess the inherent risks in the landslide catastrophe chain, we conducted a thorough analysis considering node access degree and clustering coefficients. Critical nodes driving economic losses, such as floods, debris flows, secondary landslides, and downstream water damage, were identified. Additionally, we isolated vulnerable connections within the evolving network, evaluating the susceptibility of each edge. Our research underscores the significance of proactive measures, including pre-disaster monitoring, early warning systems, and timely post-disaster information dissemination. Implementing these actions can play a pivotal role in mitigating the impact of landslide disasters, preserving lives and sustaining regional prosperity.

A continuous-time network evolution model describing $ N $-interactions

<p>We have introduced a new continuous-time network evolution model. We have described cooperation, so we have considered the cliques of nodes. The evolution of the network was based on cliques of nodes of the network and was governed by a branching process. The basic properties of the evolution process were described. Asymptotic theorems were proved for the number of cliques having a fixed size and the degree of a fixed node. The generating function was calculated, and then the probability of extinction was obtained. For the proof, advanced results of multi-type branching processes were used. Besides precise mathematical proofs, simulation examples also supported our results.</p>

A Network-gene-mutation-based model for large network dynamic application reliability evaluation

Network evolution model (NEM) is a framework proposed recently for dynamic application reliability evaluation which integrates various factors that affect application failure. The existing component-based NEM relies on an exhaustive enumeration of all potential states of network components, which can lead to a computational bottleneck, especially when dealing with large networks. Therefore, to tackle this challenge, this paper presents a Network-gene-mutation-based NEM. The model introduces novel concepts of Network-cells and Network-genes inspired by the gene-cell-function model framework used in system biology to address complex disease problems. Network-cells are constructed according to the statistical classification of components. Network-genes are constructed by the average failure rate of components within a Network-cell. Dynamic network states are simulated by the Network-gene-mutation function of Network-cells instead of the failure and repair function of network components. Validation of our proposed model is performed through the comparison with benchmark models. The results demonstrate the validity of our proposed model in calculating dynamic application reliability. Besides, the comparison results with the component-based NEM also suggest that our proposed model reduces the time complexity of the algorithm from square order [Formula: see text] to constant order [Formula: see text] and improves the simulation efficiency by roughly 104. Applied to networks of varying scales, our proposed model proves to be an efficient method for calculating dynamic application reliability in large networks. In summary, this paper offers a promising solution to address the computational bottlenecks encountered in the component-based NEM when evaluating the reliability of dynamic applications.

Degree-preserving graph dynamics: a versatile process to construct random networks

Abstract Real-world networks evolve over time via the addition or removal of vertices and edges. In current network evolution models, vertex degree varies or grows arbitrarily. A recently introduced degree-preserving network growth (DPG) family of models preserves vertex degree, resulting in structures significantly different from and more diverse than previous models ([Nature Physics 2021, DOI:10.1038/s41567-021-01417-7]). Despite its degree preserving property, the DPG model is able to replicate the output of several well-known real-world network growth models. Simulations showed that many real-world networks can also be constructed from small seed graphs via the DPG process. Here, we start the development of a rigorous mathematical theory underlying the DPG family of network growth models. We prove that the degree sequence of the output of some of the well-known, real-world network growth models can be reconstructed via the DPG process, using proper parametrization. We also show that the general problem of deciding whether a simple graph can be obtained via the DPG process from a small seed (DPG feasibility) is, however, NP-complete. It is an intriguing open problem to uncover whether there is a structural reason behind the DPG-constructability of real-world networks. Keywords: network growth models; degree-preserving growth (DPG); matching theory; synthetic networks; power-law degree distribution.

Risk accumulation assessment method for building construction based on complex network

PurposeIn the evolution process of building construction accidents, there are key nodes of risk change. This paper aims to quickly identify the key nodes and quantitatively assess the node risk. Furthermore, it is essential to propose risk accumulation assessment method of building construction.Design/methodology/approachAuthors analyzed 419 accidents investigation reports on building construction. In total, 39 risk factors were identified by accidents analysis. These risk factors were combined with 245 risk evolution chains. Based on those, Gephi software was used to draw the risk evolution network model for building construction. Topological parameters were applied to interpret the risk evolution network characteristic.FindingsCombining complex network with risk matrix, the standard of quantitative classification of node risk level is formulated. After quantitative analysis of node risk, 7 items of medium-risk node, 3 items of high-risk node and 2 items of higher-risk nodes are determined. The application results show that the system risk of the project is 44.67%, which is the high risk level. It can reflect the actual safety conditions of the project in a more comprehensive way.Research limitations/implicationsThis paper determined the level of node risk only using the node degree and risk matrix. In future research, more node topological parameters that could be applied to node risk, such as clustering coefficients, mesoscopic numbers, centrality, PageRank, etc.Practical implicationsThis article can quantitatively assess the risk accumulation of building construction. It would help safety managers could clarify the system risk status. Moreover, it also contributes to reveal the correspondence between risk accumulation and accident evolution.Originality/valueThis study comprehensively considers the likelihood, consequences and correlation to assess node risk. Based on this, single-node risk and system risk assessment methods of building construction systems were proposed. It provided a promising method and idea for the risk accumulation assessment method of building construction. Moreover, evolution process of node risk is explained from the perspective of risk accumulation.

On evolving network models and their influence on opinion formation

In this paper, we propose a new model for continuous time opinion dynamics on an evolving network. As opposed to existing models, in which the network typically evolves by discretely adding or removing edges, we instead propose a model for opinion formation which is coupled to a network evolving through a system of ordinary differential equations for the edge weights. We interpret each edge weight as the strength of the relationship between a pair of individuals, with edges increasing in weight if pairs continually listen to each other’s opinions and decreasing if not. We investigate the impact of various edge dynamics at different timescales on the opinion dynamics itself. This is done partly through analytic results and partly through extensive numerical simulations of two case studies: one using bounded confidence interaction dynamics in the opinion formation process (as in the classical Hegselmann–Krause model) and one using an exponentially decaying interaction function. We find that the dynamic edge weights can have a significant impact on the opinion formation process, since they may result in consensus formation but can also reinforce polarisation. Overall, the proposed modelling approach allows us to quantify and investigate how the network and opinion dynamics influence each other.

A model of temporal and spatial river network evolution with climatic inputs

Predicting the temporal and spatial evolution of the river network is part of the Earth's critical zone investigations, which has become an important endeavor. However, modeling integration of the river network and critical zone over millions of years is rare. We address the problem of how to predict integrated river length development as a function of time within a framework of addressing the critical zone depth as a function of time. In case of groundwater-river interaction, we find a non-linear spatio-temporal scaling relationship between time, t, and total river length L, given by t≈Lp with power p being near 1.2. The basis of our model is the presumption that groundwater flow paths are relevant to river integration. As river integration may proceed over disconnected basins with irregular relief, the relevant optimal subsurface flow paths are proposed to be defined within a 3D network, with optimal path exponent 1.43. Because the 2D model of the river length has already been shown to relate to a power of the Euclidean distance across a drainage basin with the predicted universal optimal path exponent from percolation theory, Dopt = 1.21, the optimal groundwater paths should relate to the surface river length with an exponent equaling the ratio 1.43/1.21 = 1.18. To define a predictive relationship for the river length, we need to use specific length and time scales. We assume that the fundamental specific length scale is a characteristic particle size (which is commonly used to define the pore scale flow network), and the fundamental time scale is the ratio of the particle size to the regional groundwater flow rate. In this paper, we consider cases of predicting spatio-temporal scaling of drainage organization in the southwestern USA–the Amargosa, Mojave, Gila (and its tributaries) and the Rio Grande, and Pecos Rivers. For the Mojave and Gila Rivers, theoretical results for time scales of river integration since ca. 10 Ma are quite predictive, though the predicted time scales exceed observation for the Rio Grande and Pecos.

Availability Model for Data Center Networks With Dynamic Migration and Multiple Traffic Flows

The evaluation of application availability is a crucial step in ensuring functionality of the data center network (DCN). The application of data center network is no more a chain of physical devices, but a chain of virtualized network function (VNF) dynamically installed in servers, i.e., the service function chain (SFC), due to the adaptation of network function virtualization (NFV). When dynamic migration is considered, the application availability evaluation would encounter problems since the state of VNF components is not independent. And in the application deployed in DCN, there is multiple traffic flow because of the serialization of 1+1 (active-standby) and N-way (load-sharing) redundant VNF, however only single flow is considered in current availability model. When dynamic migration and multiple traffic flows are considered, the application availability evaluation would encounter the following problems: 1) The application behavior is not described in the existing availability model; 2) The availability model could not quantify the influence of different traffic of each flow. This paper proposes an availability model, which consist of network evolution model (NEM) of DCN and the availability evaluation algorithm based on NEM (AEA-NEM), to evaluate the availability of different applications deployed on DCN. The NEM of DCN describes the dynamical process of migration and switch of three kinds of VNF (normal, 1+1 and N-way) in application, and the AEA-NEM evaluate the availability of application with multiple traffic flows. The simulation results show that, the steady state availability of the proposed model is consistent with it of generalized stochastic Petri net (GSPN) model. And the model could evaluate the application availability with dynamic migration and multiple traffic flows in DCN. Based on the current VNF placement and redundant strategy, the most available designing scheme is analyzed.

TIEN: Temporal interest-aware evolution model for “Next Item Recommendation”

The “next item recommendation” is used to predict the next item that a user may be interested in and its main challenges is identifying the user's interests. The current state-of-the-art methods mostly adopt time-series-based recommendation methods but ignore the role of time intervals in the interest evolution process. This motivated us to propose a new Time Interest-aware Evolutionary Network model (TIEN) to predict the next item that a user may be interested in. TIEN highlights the role of time intervals in the interest evolution process and divides interests into long-term interests and short-term interests. In TIEN: (1) We devise a time interval-based attention network TAN to capture the user's interests, which employs item embeddings, behavior sequence embeddings, and time intervals to calculate the user's interest attention; (2) We devise an ATT-GRU network based on candidate activation vectors to capture the interest evolution process, which skillfully leverages the reset gate, hidden layer state, item information, time intervals, etc. to participate in interest evolution; (3) We introduce a dual-network collaborative training mechanism, where the obtained interests of users are treated as weights to guide the interest evolution process. Experimental results on multiple benchmark datasets show that TIEN has higher performance than the current state-of-the-art methods.

Universal patterns in egocentric communication networks

Tie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at the network and individual levels. Egocentric networks, networks of relationships around an individual, exhibit few strong ties and more weaker ties, as evidenced by electronic communication records. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and driving mechanisms of social tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets of interactions between millions of people during months to years. We find universality in tie strength distributions and their individual-level variation across communication modes, even in channels not reflecting offline social relationships. Via a simple model of egocentric network evolution, we show that the observed universality arises from the competition between cumulative advantage and random choice, two tie reinforcement mechanisms whose balance determines the diversity of tie strengths. Our results provide insight into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior.