Evolution Of Network Structure Research Articles

Facet-dependent electrocatalytic oxygen evolution of porous network structure Ni-Fe-O materials

Constructing unique crystal facets can increase active sites and enhance electronic structure for electrocatalysts towards the oxygen evolution reaction (OER). Here, we successfully achieved the transformation between the (311) and (222) facets of the NiFe2O4 catalysts by adjusting the ratio of Ni and Fe elements, and it was found that the activity in OER is enhanced with the exposure of (311) facet. The optimal catalyst shows a lower overpotential of 238 mV at 100 mA/cm2 current density and a small Tafel slope of 26.4 mV/dec. Furthermore, benefiting from the porous network structure, the unique super hydrophilic surface and the low-coordination Fe3+ atoms characteristic, the catalyst has an efficient mass transfer channel and fast charge transfer in the electrocatalytic OER process. More metal-O clusters were exposed on the (311) facet surface as the active site, which further improved the catalytic activity. This work presents a successful approach to creating highly active spinel catalysts through facet-engineering for electrocatalytic water splitting.

Synergy of Inter-Regional Knowledge Innovation System from the Perspective of Industry-Science-Research Collaborative Networks

Abstract Knowledge innovation networks formulated by the collaborations between entities (industry department, school, research center and laboratory) evolving over time, varying by regions, is an indicator of inter-regional knowledge innovation linkages. In this work, we proposed a novel synergy analysis approach, taking the structural value of industry-science-research collaborative network as knowledge innovation subsystem parameter. The changing of this parameter indicates the synergy of Inter-Regional Knowledge Innovation System (IRKIS), incorporating the network structure evolution and systematic collaboration, also analyzing the IRKIS synergy capability. We collected 18,776 articles from 2009–2020 that are related to Alzheimer’s disease participating in China from Web of Science, and 7,941 patent data from the USTPO database. Then, we extracted knowledge innovation departments based on the dimensions of industry-science-research, constructed inter-regional knowledge innovation network, selected Degree Centrality, Close Centrality and Structure Hole as parameters of industry-science, industry-research, and science-research subsystems to analyze the synergy degree of IRKIS. The results indicated that evolution law of IRKIS could be explained through the perspective of industry-science-research collaborative network. The trend of more international collaborations illustrated by the rapid increasement of inter-regional knowledge innovation network parameter, however, the average synergy level of IRKIS in Alzheimer’s research is still low.

Continuous-Time Graph Representation with Sequential Survival Process

Over the past two decades, there has been a tremendous increase in the growth of representation learning methods for graphs, with numerous applications across various fields, including bioinformatics, chemistry, and the social sciences. However, current dynamic network approaches focus on discrete-time networks or treat links in continuous-time networks as instantaneous events. Therefore, these approaches have limitations in capturing the persistence or absence of links that continuously emerge and disappear over time for particular durations. To address this, we propose a novel stochastic process relying on survival functions to model the durations of links and their absences over time. This forms a generic new likelihood specification explicitly accounting for intermittent edge-persistent networks, namely GraSSP: Graph Representation with Sequential Survival Process. We apply the developed framework to a recent continuous time dynamic latent distance model characterizing network dynamics in terms of a sequence of piecewise linear movements of nodes in latent space. We quantitatively assess the developed framework in various downstream tasks, such as link prediction and network completion, demonstrating that the developed modeling framework accounting for link persistence and absence well tracks the intrinsic trajectories of nodes in a latent space and captures the underlying characteristics of evolving network structure.

Evolution game model and simulation of enterprise R&D cooperative innovation network under network externalities

ABSTRACT Multi-agent cooperation, pooling innovative resources, helps to develop key technologies and major projects in enterprises. However, the multi-agent cooperation of enterprises makes the cooperative relationship unstable, and behaviours such as moral hazard, spillover of technology or information are more likely to occur, thereby increasing the risk of enterprises. In this research an enterprise R&D cooperative evolutionary game model is built to investigate the influence of Network externalities on enterprise R&D cooperation in innovation networks. Furthermore, the evolution of innovative network structure of game results is simulated by means of computer technology so that the effects of network externalities on corporation degree, agglomeration and network structure are analyzed. The results show that Network externalities led to a decrease of cooperated partners, a decline in cooperation degree and agglomeration of enterprises, and a shrinking enterprise R&D cooperation tendency. The influence of network externalities shows significant structural differences in the three types of enterprise networks: the degree of enterprise cooperation and agglomeration fluctuates greatly in the scale-free network, but slightly in the small-world network and the random network.

Numerical Simulation Study on the Mechanics and Pore Characteristics of Tectonically Deformed Coal under Multi-Level and Multi-Cycle Loading and Unloading Conditions

Horizontal well cavern completion and stress release is considered a potential technique for efficient development of coalbed methane in tectonically deformed coal (TDC). Pulsating loading and unloading is a key technique for the controlled expansion of caverns and broader stress release within the reservoir. However, current understanding of the mechanical characteristics and pore network structure evolution of TDC under cyclic loading and unloading conditions is still limited. This paper employs numerical simulation methods to study the mechanical behavior and damage characteristics of TDC under cyclic loading and unloading. After obtaining a set of micromechanical parameters reflecting the behavior of TDC samples under triaxial compression in high-stress states, the effects of different stress gradients and cyclic amplitudes on the stress–strain curve, porosity changes, and crack propagation in TDC samples were analyzed. The study results indicate that under various cyclic loading and unloading conditions, the mechanical response characteristics of TDC samples are broadly similar, primarily divided into compression, slow expansion, and accelerated expansion phases. Under low unloading level conditions, the volume expansion of TDC samples is minimal. Also, at the same unloading level, the strain increment decreases with an increasing number of cycles. Correspondingly, under these conditions, the porosity and microcrack expansion in TDC are less than in high-stress gradient scenarios. Under the same unloading level but different amplitudes, the volume expansion rate at 50% unloading amplitude is higher than at 1 MPa unloading amplitude for TDC, with an increased number of crack expansions. Therefore, under cyclic loading conditions, the sensitivity of crack propagation within TDC samples to amplitude is greater than that to unloading level. Under actual pulsating excitation conditions, a low-amplitude, low-stress gradient pulsation method should be used to maintain the stability of horizontal well caverns, and gradually increase the cyclic amplitude to achieve the efficient extraction of coalbed methane in TDC reservoirs. The findings of this study can serve as an important reference for optimizing process parameters in cyclic pulsating stress release engineering for TDC.

Influence analysis of network evolution on Parrondo effect

Parrondo's paradox is a scheme used to describe an interesting paradoxical situation that a losing Game A and a losing Game B played randomly or periodically will produce a winning result. Here, a dynamic process of network evolution of Link A + Game B is proposed to yield the Parrondo effect. Game B with two asymmetric branches depends on the relative comparison between the capital of the network node and the average capital of all its neighbors. Simulation results demonstrate that network structure evolution make the losing Game B produce a paradoxical effect of winning and would be advantageous to the development of the “ratcheting” mechanism in Game B. Furthermore, the underlying paradoxical mechanism is analyzed to illustrate the parameter space where the “strong” Parrondo effect occurs. Then influence of two types of network connection is analyzed, indicating that the “agitating” effect of the network is basically the same when a node connects to a neighbor's neighbor or randomly chooses a node other than its neighbors. Further, a higher frequency of network evolution yields a larger parameter region where the “strong” Parrondo effect emerges.

Evolution of spatial network structure for land-use carbon emissions and carbon balance zoning in Jiangxi Province: A social network analysis perspective

Exploring the spatial network structure of land-use carbon emissions (LUCE) and the carbon balance in developing regions is pivotal for climate change mitigation in these areas. Using socio-economic and land-use data from 2000 to 2020, this study focused on Jiangxi Province as a representative case to elucidate the spatial network structure of LUCE and conduct carbon balance zoning. The primary findings were as follows: (1) The LUCE distribution in Jiangxi Province demonstrated a spatial pattern with high values in the northwest and low values in the southeast. An increasing trend was observed in the total amount of LUCE. Furthermore, there were evident regional differences in the Ecological Support Coefficient (ESC), indicating a weakening regional carbon sink capacity. (2) The spatial network structure of LUCE was intricate but stable. Although the gravitational interactions of LUCE between cities have intensified, overall network connectivity remained moderately correlated, indicating a prominent regional development imbalance. (3) There was a pronounced “core-periphery” structure in the LUCE spatial correlation network. Northern Jiangxi was the “core” of the network, taking an “initiator” role, whereas most cities in southern Jiangxi played a “passive” role, and resided at the “periphery” of the network. The inter-regional LUCE network exhibited pronounced spatial spillover, with inter-block correlations surpassing those within blocks. (4) By leveraging empirical data and spatial metrics, we categorized Jiangxi Province into five distinct carbon balance zoning types and subsequently proposed land-use optimization strategies. From the perspective of social network analysis (SNA), this study offers methodological insights into low-carbon development and synergistic emission reduction in developing regions.

Military Exercises and Network Effects

Abstract Between 1980 and 2016, the percentage of states that partnered in a multinational military exercise (MME) increased twenty-fold. What explains this proliferation? Existing studies focus on the role of major powers and polarity but fail to explain exercises without great powers or the continuous growth of MME participation. I conceptualize patterns of exercises among all members of the international system as networks. Inferential network analysis shows that higher-order effects like popularity, transitivity, and memory increase the probability that states cooperate militarily. Countries with many connections have institutional knowledge and prestige to attract partners. Multinational coalitions form where mutual friends increase trust and create positive feedback. Past cooperation lowers the costs of future partnerships. Empirical analysis shows that the evolving network structure of MMEs is an emergent property driven by these interdependent processes, and that traditional explanations for security cooperation like great powers and alliances decrease in influence over time.

Asymmetric game promotes the emergence of cooperation in dynamic networks

Individuals in networked social groups often exhibit asymmetry when interacting with each other due to enjoying different amounts of resources and having different numbers of neighbors. In this paper, we study the impact of asymmetric games on the evolution of cooperation in dynamic networks. By constructing linking dynamics, the evolution of network structure is analyzed, and further the coevolution of strategy and network structure is explored. Based on three different contribution modes in the public goods game, it is revealed that compared with symmetric game, asymmetric game may be more conducive to promoting the emergence and maintenance of cooperation, while the emergence of cooperation benefits from the dynamic adjustment of network structure. We investigate the influence of the adjustment rules of dynamic networks on the evolution of cooperation, and show the difference of cooperation level under different social connection preferences. By analyzing the time scale of strategy updating compared with network link adjustment, it is pointed out that faster network evolution is beneficial to cooperation. In addition, we explore the influence of some statistical characteristics of networks on the system evolution.

DyLFG: A Dynamic Network Learning Framework Based on Geometry.

Dynamic network representation learning has recently attracted increasing attention because real-world networks evolve over time, that is nodes and edges join or leave the networks over time. Different from static networks, the representation learning of dynamic networks should not only consider how to capture the structural information of network snapshots, but also consider how to capture the temporal dynamic information of network structure evolution from the network snapshot sequence. From the existing work on dynamic network representation, there are two main problems: (1) A significant number of methods target dynamic networks, which only allow nodes to increase over time, not decrease, which reduces the applicability of such methods to real-world networks. (2) At present, most network-embedding methods, especially dynamic network representation learning approaches, use Euclidean embedding space. However, the network itself is geometrically non-Euclidean, which leads to geometric inconsistencies between the embedded space and the underlying space of the network, which can affect the performance of the model. In order to solve the above two problems, we propose a geometry-based dynamic network learning framework, namely DyLFG. Our proposed framework targets dynamic networks, which allow nodes and edges to join or exit the network over time. In order to extract the structural information of network snapshots, we designed a new hyperbolic geometry processing layer, which is different from the previous literature. In order to deal with the temporal dynamics of the network snapshot sequence, we propose a gated recurrent unit (GRU) module based on Ricci curvature, that is the RGRU. In the proposed framework, we used a temporal attention layer and the RGRU to evolve the neural network weight matrix to capture temporal dynamics in the network snapshot sequence. The experimental results showed that our model outperformed the baseline approaches on the baseline datasets.

Evolution, Forecasting, and Driving Mechanisms of the Digital Financial Network: Evidence from China

Digital finance (DF) is the engine driving financial inclusion worldwide, but the current uneven development of DF across regions would hinder this process. Based on cross-sectional data from 288 prefecture-level cities for the representative years 2011, 2014, 2017, and 2020, this paper uses geographic detector methods, social network analysis, and geographical and temporal weighted regression (GTWR) to explore the key drivers of urban DF, revealing and forecasting the DF network structural evolution and its driving mechanism. The results show that (1) economic level, traditional financial level, internet popularity, innovation level, and government intervention are the key drivers of DF development. (2) During the decade, the proportion of high-intensity urban interconnections increased from 3.3% to 12.3%. Most cities are at a low level of intensity, showing a polarization trend. (3) The cities with high betweenness centrality are concentrated in the megacities and the number is stable at 5. The structure of network communities is relatively stable, with the number reduced to 10. Cities with the greatest possibility of connection are located in the Pearl River Delta (PRD) and the Yangtze River Delta (YRD), accounting for 60% of the total. (4) The drivers of DF development present significant spatial heterogeneity over time. The traditional financial level shows a positive and continuous promoting effect, while government intervention plays a negative role.

Sustainable Water Policymaking for the Hotel Industry: A Longitudinal Network Analysis of Policy Documents

Safeguarding water resources in tourism contributes to the achievement of the United Nations Sustainable Development Goals (SDGs) and to the wider 2030 Agenda for Sustainable Development. However, sustainable management of water, in order to address both current and future demand for water resources in an equitable and environmentally sound manner, is one of the most significant challenges faced by tourist destinations. Using a case study approach, this research conducts a social network analysis (SNA) of the water policymaking system of Singapore’s hotel industry by longitudinally examining the structure of relations among policy themes and policy stakeholders. The network approach attempts to examine the structural evolution in the water demand management (WDM) policy network based on an analysis of 329 (co)produced policy documents (2001–2020) available in the public domain. A broad range of policy themes, together with divergent policy stakeholders, are uncovered in this interconnected dynamic policy system. Subsequently, systematic policy research is extended to discuss the interrelationships of policy stakeholders and policy themes over the past 20 years. Our findings highlight the network structure of the policy domain and suggest an openness and vibrancy of sustainable WDM as an interdisciplinary policy domain. Cross-sectoral stakeholder engagement has contributed to the evolution and complexity of the policy theme network. The study’s findings provide significant insights into the relationships of policy actors and the evolving network structure of a multidimensional policy framework.

Collaborative innovation of wireless communication in logistics: evolution of network structure and knowledge domain

Collaborative innovation of wireless communication in logistics: evolution of network structure and knowledge domain

Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle

Link prediction for temporal heterogeneous networks is an important task in the field of network science, and it has a wide range of real-world applications. Traditional link prediction methods are mainly based on static homogeneous networks, which do not distinguish between different types of nodes in the real world and do not account for network structure evolution over time. To address these issues, in this paper, we study the link prediction problem in temporal heterogeneous networks and propose a link prediction method for temporal heterogeneous networks (LP-THN) based on the information lifecycle, which is an end-to-end encoder–decoder structure. The information lifecycle accounts for the active, decay and stable states of edges. Specifically, we first introduce the meta-path augmented residual information matrix to preserve the structure evolution mechanism and semantics in HINs, using it as input to the encoder to obtain a low-dimensional embedding representation of the nodes. Finally, the link prediction problem is considered a binary classification problem, and the decoder is utilized for link prediction. Our prediction process accounts for both network structure and semantic changes using meta-path augmented residual information matrix perturbations. Our experiments demonstrate that LP-THN outperforms other baselines in both prediction effectiveness and prediction efficiency.

AN EVOLUTIONARY MODEL OF SOCIAL NETWORK STRUCTURE DRIVEN BY INFORMATION INTERACTION

The interaction of information and the evolution of network structure are inseparable. In order to construct social network evolution and information propagation models that better fit real-world scenarios, this paper proposes a social network structure evolution model driven by changes in the strength of relationships between individuals through their information interactions with each other. During the evolution process of the network, information interaction between individuals is also influenced by the network structure. Therefore, we improve traditional propagation models and construct an information propagation model with dynamic propagation rates. The proposed model is used to simulate both the spread of information and the evolution of network structures in real social networks. Finally, simulation results are compared to real-world data, demonstrating the effectiveness and rationality of the proposed model.

Traceability of abnormal energy consumption modes in grinding systems based on evolution analysis of causal network structure

Abnormal energy consumption mode tracing is used to locate the root cause of the system deviating from the normal energy consumption mode (ECM), in order to support operators in understanding system abnormal changes and abnormal propagation trends. Due to the compensatory effect of closed-loop control systems on external interference and the influence of processes such as coarse powder regrinding and circulating air utilization during grinding, there is a strong correlation between parameters, and anomalies are easily propagated between parameters. Tracing the root cause variable of anomalies is challenging. Previous abnormal ECM tracing ignored the dynamic changes in causal relationships between coupled variables in the circulation process, and had problems with strong expert knowledge dependence and slow response. Therefore, this paper proposes an abnormal ECM tracing method based on causal network structure evolution analysis. It proposes directed symbolic transfer entropy to measure variable causal relationships, and combines the advantages of complex networks in characterizing complex system dynamic characteristics to construct an ECM causal network. By analyzing network structure characteristics, it locates the root cause variables of abnormal ECMs and infers propagation paths. Finally, through the application of six types of abnormal scenarios in actual slag grinding processes, it verifies the accuracy of the proposed method in locating abnormal roots and identifying propagation paths.

Spatial and temporal evolution of Guangdong tourism economic network structure from the perspective of social networks

This study uses social network analysis and modified gravity model methods to empirically analyse the network spatial correlation structure and spatiotemporal development trend of 21 cities in Guangdong Province from 2000 to 2020 based on tourism economic development data. The findings show that, first, Zhuhai has the greatest potential for growth as the centre of the spatial and temporal evolution trend of the network structure of the tourism economy in Guangdong Province, ahead of Shenzhen, Huizhou, Zhaoqing, Zhongshan, Jiangmen and Dongguan. However, Guangzhou, the capital city of Guangdong Province, is experiencing a decline in such influence and development. Second, there is a counter-trend growth in the number of tourism-related economic links among the 21 cities. Although Guangdong's tourism economic network intensity is strong, there is still room for further optimisation. Third, the results of the overall network indicators show that there is a need for further improvement in network density, grade and efficiency to help reduce the relative development gap of the cities' tourism and effectively improve the overall development of Guangdong's tourism economy. Finally, based on the core–periphery structure, this study proposes relevant suggestions for the sustainable development of Guangdong's tourism industry.

Industry–university–research knowledge flow in energy subjects: evolution of network structure in collaborative photovoltaic innovations

This data report collected photovoltaic patents under the Industry-University-Research (IUR) collaborative innovation perspective. The evolution of the collaborative innovation network from 2000 to 2019 was studied. The characteristics of patent holders and overall network characteristics was analysed based on Social Network Analysis (SNA), including network size and degree, network density, and network degree centrality. The SNA results show that the network size and degree have increased significantly over time. The network density has become increasingly sparse as PV technology has developed and the number of core network members has increased. Universities and research institutions is gradually shown its clustering role in leading innovations and discipline construction. H01L is the leading knowledge domains studied by the industry, university, and research institutions. F24J and F24S were the second most frequently involved knowledge domains in each stage. Furthermore, two noteworthy knowledge domains are H02J and H02S, which are related to control and regulation of power systems. This analysis provides insights into the energy subjects and knowledge domains within collaborative innovations and education.

Preferential selection to promote cooperation on degree–degree correlation networks in spatial snowdrift games

Degree–degree correlation is an important topological characteristic of real-world networks and has been shown to have a significant effect on cooperation in a population. Meanwhile, the preferential selection of individuals could be exploited as an effective mechanism to improve cooperation in evolutionary games. Network structure and strategy evolution are two essential factors affecting the level of cooperation in spatial evolutionary games, and by considering both of them, proposed here is a spatial snowdrift game with preferential selection on scale-free networks with degree–degree correlation. How two main parameters—the Pearson correlation coefficient r and the intensity of preferential selection δ—affect the cooperation is investigated, and simulation results show that when preferential selection is involved in evolutionary games, then larger δ can give a higher level of cooperation. Importantly, an optimal value of r is found that gives maximum cooperation in the population, and how r and δ affect the cooperation is found to depend on the cost parameter of the game. Furthermore, corresponding microcosmic explanations are provided based on the quantity χ, the time series of links, and the strategy distribution.

Phosphorus-Doped directly interconnected networks of amorphous Metal-Organic framework nanowires for efficient methanol oxidation

Developing high-performance and low-cost electrocatalysts toward methanol oxidation reaction (MOR) is essential for fuel cell applications. Herein, we report a defect engineering strategy integrating amorphization and phosphorization to construct directly interconnected networks of amorphous NiCo-based metal–organic framework nanowires (a-NiCo-MOFNWs) with phosphorus (P) doping. The resulting P-doped a-NiCo-MOFNWs (a-NiCo-MOFNWs-P) network displays superior MOR efficiency and long-term durability over 1000 cyclic voltammetry (CV) measurements. The special structure of directly interconnected networks and the synergistic effect between the amorphous MOFs and dispersed phosphorus species give rise to abundant exposed active sites, accelerated electron transport, and increased porosity for mass transfer, thus boosting the reaction kinetics of MOR. This work provides additional insights into the network assembly and structural evolution of one-dimensional (1D) MOFs, and also opens up new avenues for the design of highly reactive and robust non-precious metal-based electrocatalysts.