Network Structure Characteristics Research Articles

Properties of the forgotten index in bipolar fuzzy graphs and applications.

Topological indices are the numbers that remain constant under graph automorphism. Topological indices describe a network's connectivity, structure, and topological characteristics. These indices have many applications in crisp graphs. However, in many cases, it is observed that some situations can't be described using the idea of crisp graphs. So, to overcome this issue, the need to define topological indices for fuzzy and bipolar fuzzy graphs arises. The F-index, or the Forgotten Index, is a significant topological index. A bipolar fuzzy graph with two opposite-sided opinions of both the edges and vertices measures the impreciseness or uncertainties of the edges and vertices along the positive and negative sides. In this article, we have presented the Forgotten Index for bipolar fuzzy graphs. Then, we have proved some theorems regarding the F-index of numerous types of bipolar fuzzy graphs, such as regular bipolar fuzzy graphs, complete bipolar fuzzy graphs, etc., the bounds of the F-index in bipolar fuzzy graphs, and the relationships of the F-index with other topological indices in bipolar fuzzy graphs. We have applied the proposed topological index, the F-index for bipolar fuzzy graphs, to matrimonial websites to find potential life partners based on compatibility and discussed the application of the Forgotten Index in gene regulatory networks.

Cortical structural network characteristics in non-cognitive impairment end-stage renal disease

ObjectiveExplore alterations in topological features of gray matter volume (GMV) and structural networks in non-cognitive impairment end-stage renal disease (Non-CI ESRD).Materials and methodsUtilizing graph theory, we collected structural magnetic resonance imaging (sMRI) data from 38 Non-CI ESRD patients and 50 normal controls (NC). We compared, and extracted the GMV across subject groups, constructed corresponding structural covariance networks (SCNs), and investigated the alterations in SCNs feature parameters between groups.ResultsIn Non-CI ESRD patients, The GMV were reduced in several brain regions, predominantly on the left side (p < 0.05, FWE correction). The small-world network characteristics of the patient group’s brain networks showed a tendency toward regular. In a few densities, global network parameters, transitivity, (p < 0.05) was significantly increased in the ESRD group. Regional network measurements revealed inconsistent changes in regional efficiency across different brain areas. In the analysis of network hubs, the right temporal pole is likely a compensatory hub for Non-CI ESRD patients. The SCNs in Non-CI ESRD patients demonstrated reduced topological stability against targeted attacks.ConclusionThis study reveals that patients with renal failure exhibited subtle changes in brain network characteristics even before a decline in cognitive scores. These changes involve compensatory activation in certain brain regions, which enhances network transitivity to maintain the efficiency of whole-brain network information integration without significant loss. Additionally, the SCNs characteristics can serve as a neuroanatomical marker for brain alterations in Non-CI ESRD patients, offering new insights into the mechanisms of early brain injury in ESRD patients.

Computing the Kirchhoff index of a family of phenylene chain networks

Abstract The Kirchhoff index is a fundamental topological metric that provides insights into the structural and electrical characteristics of networks. It is defined as the sum of resistance distances between all pairs of nodes, serving as a key factor in understanding the dynamics within networks. To investigate the impact of structural variations on the Kirchhoff index, we select a family of phenylene chain networks as our model and establish a methodology to explore the Kirchhoff index using the Laplacian spectrum. By analyzing the network structure, we introduce a parameter to control the number of iterations, providing a recursive relationship between the Laplacian matrix and its eigenvalues at intervals of generations. This approach enables the derivation of an analytical expression for both the sum of the reciprocals of all nonzero Laplacian eigenvalues and the Kirchhoff index.

Constructing representative group networks from tractography: lessons from a dynamical approach

Human group connectome analysis relies on combining individual connectome data to construct a single representative network which can be used to describe brain organisation and identify differences between subject groups. Existing methods adopt different strategies to select the network structural features to be retained or optimised at group level. In the absence of ground truth, however, it is unclear which structural features are the most suitable and how to evaluate the consequences on the group network of applying any given strategy. In this investigation, we consider the impact of defining a connectome as representative if it can recapitulate not just the structure of the individual networks in the cohort tested but also their dynamical behaviour, which we measured using a model of coupled oscillators. We applied the widely used approach of consensus thresholding to a dataset of individual structural connectomes from a healthy adult cohort to construct group networks for a range of thresholds and then identified the most dynamically representative group connectome as that having the least deviation from the individual connectomes given a dynamical measure of the system. We found that our dynamically representative network recaptured aspects of structure for which it did not specifically optimise, with no significant difference to other group connectomes constructed via methods which did optimise for those metrics. Additionally, these other group connectomes were either as dynamically representative as our chosen network or less so. While we suggest that dynamics should be at least one of the criteria for representativeness, given that the brain has evolved under the pressure of carrying out specific functions, our results suggest that the question persists as to which of these criteria are valid and testable.

Oncology Physician Turnover in the United States Based on Medicare Claims Data

Objective: Physician turnover rates are rising in the United States. The cancer workforce, which relies heavily on clinical teamwork and care coordination, may be more greatly impacted by turnover. In this study, we aimed to characterize oncologists who move to identify targets for recruitment and retention efforts. Methods: We identified medical, radiation, and surgical oncologists who treated Medicare beneficiaries diagnosed with breast, colorectal, or lung cancer in 2016–2019. We used multivariable logistic regression to identify physician-level and multivariable multinomial regression to identify region-level characteristics associated with turnover. Measures included demographic, practice, and patient-sharing network characteristics. Results: Our cohort included 25,012 medical, radiation, and surgical oncologists, of which, 1448 (5.8%) moved. Women [vs men; odds ratio (OR): 1.46; 95% CI: 1.30–1.64] and surgeons (vs medical oncologists; OR: 1.17; 95% CI; 1.04–1.33) had higher odds of moving. Compared with oncologists with moderate patient-sharing ties, those with many ties had lower odds of moving (OR: 0.55; 95% CI: 0.43–0.70). Patient-sharing networks with low efficiency (vs moderate) were more likely to have a net loss in their oncology workforce (OR: 3.06; 95% CI: 1.12–8.35), whereas those with low specialist vulnerability (vs moderate) were less likely to have a net loss (OR: 0.32; 95% CI: 0.1–0.99). Conclusions: This study identified novel patient-sharing network characteristics associated with turnover, providing new insights into how the structural features of patient-sharing networks may be related to the recruitment and retention of oncologists.

The relationship between LGBT older adults’ social network structure and function

Social prejudice, family nonacceptance, and lack of marriage or adoption rights often restrict the formation of conventional social ties among LGBT older adults. As a result, they often rely on families of choice constituted by other LGBT/age peers for support. Using a series of regression analyses with a national sample of LGBT older Americans, this study investigated how characteristics of social network structure (size and composition - LGBT vs. non-LGBT identity and age ≥50 vs. < 50) were related to LGBT older adults’ social network function (perceived social support). Larger social networks, especially those composed of LGBT members and older adults, were associated with more perceived social support. In subgroup analyses, larger social networks composed of more LGBT members and older adults were associated with more perceived social support among lesbian and gay respondents. For bisexual + older adults, larger social network size was associated with more perceived social support, but social network composition was not related to more perceived social support. For transgender older adults, neither social network size nor network composition were significantly associated with perceived social support. This study demonstrated that having more people with similar characteristics in their social networks cues LGBT older adults about the availability of social support. Findings may inform strategies for tailoring programs intended to maintain and strengthen LGBT older adults’ social support networks.

Spatial correlation effect of haze pollution in the Yangtze River Economic Belt, China.

With the rapid development of industry, haze pollution has become an urgent environmental problem. This study innovatively utilizes network-based methods to investigate the spatial correlation effects of haze pollution transmission between urban clusters in the Yangtze River Economic Belt. A spatial correlation network of haze pollution in the Yangtze River Economic Belt was constructed using 328 urban meteorological data collection points as research samples, and its structural characteristics were examined. Main findings are as follows: (1) The spatial correlation network of PM2.5 in the Yangtze River Economic Belt urban agglomeration exhibits typical network structural characteristics: obvious spatial correlation within the network. (2) Chengdu, Chongqing, Wuhan, Nanchang, Yichang, Changsha and Yueyang are located at the center of the spatial network. They have more receiving and sending relationships. (3) 36 cities can be divided into four types: bilateral overflow, net beneficiary, net overflow and broker. Each type has different functional characteristics and linkage effects in the network. (4) Haze pollution positively correlates with the city's synergistic development capacity and urbanization rate, the higher the city's development level and the higher the Urbanization rate, the stronger its haze pollution capacity. This study provides new insights into the study of the spatial correlation and impact of haze pollution.

Topological analysis of the three-dimensional radiodensity distribution of fish otoliths: Point sampling effects on dimensionality reduction

Otoliths are calcified structures found in the inner ears of teleost fish, pivotal in marine biology for studies on metabolism, age, growth, and the identification of fish stocks, potentially leading to sustainable management practices. An important feature of this structure is its density, as it corresponds to modifications in the crystalline form of calcium carbonate during the fish's lifetime, resulting in variations in its final shape. The internal and external 3D radiodensity of otoliths from different species was obtained utilizing micro-computed tomography, however, an appropriate methodology for describing and conducting comparative studies on these data appears to be absent in the current body of literature. Therefore, we study otolith density variations from 3D computed tomography images, employing the Ball Mapper technique of Topological Data Analysis. We focus on reducing the computational cost of this analysis by applying probabilistic sampling and assessing its effects on the density variations provided by the Ball Mapper graph. To determine the sample size, we used the topology to establish what we term "Topological Sample Validation", which provided the minimum resolution with the same density information as raw data. Sample representativeness was validated through non-parametric statistical tests on the density variable. Based on the network's structural characteristics, network properties allowed for evaluating similarity between graphs. Besides the small sample size, remarkable correlations were obtained between age and network variables. Additionally, the Ball Mapper technique proved effective as a preprocessing algorithm for tomographic images, enabling the segmentation of undesired features in the object of interest.

Expansion and Exploration of the Superdiffuser Model With Agent-Based Modeling

The superdiffuser model predicts that the diffusion of a new behavior can be accelerated if superdiffusers (people who are connectors, persuaders, and mavens) are recruited to promote the behavior. We propose an expanded model where the importance of these traits varies by network structure and other network member characteristics. We assessed the plausibility of these proposed moderators using a simulation of an agent-based model in which each moderator was varied along with the superdiffuser traits of those initially seeded with the behavior. Analysis of the results of those simulations showed support for many, but not all, of the predictions as well as several surprising results that are suggestive for future empirical research.

Connections and Spatial Network Structure of the Tourism Economy in Beijing–Tianjin–Hebei: A Social Network Perspective

In the context of the coordinated development of Beijing–Tianjin–Hebei, examining the spatial connections and network structure characteristics of the tourism economy holds substantial theoretical and practical significance. This study constructs a tourism economic quality evaluation index system from four dimensions and optimizes the tourism economic gravity model by incorporating time distance and weight factors to dynamically simulate the intensity of tourism economic spatial connection (ITESC). Based on the new perspective of relational data, social network analysis is employed to investigate the spatial correlation network characteristics and group structure changes in the tourism economy within the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). The results demonstrate a significant improvement in overall tourism economic quality from 2013 to 2022, with the average quality increasing from 0.128 in 2013 to 0.157 in 2022. Tourism economic spatial connections are prevalent among cities, exhibiting heterogeneous characteristics, characterized by density in the north and relatively sparse in the south. The network cohesion of ITESC strengthens, and the network structure evolves from a highly centralized to a multi-centralized configuration, with increasingly frequent cooperation and interaction in tourism between cities. Beijing, Tianjin, and Shijiazhuang occupy central positions in the network, exhibiting strong ITESC and a high degree of connectivity, generating a significant “trickle-down effect” on peripheral cities. Consequently, strengthening ITESC between peripheral cities and core areas, as well as enhancing the bidirectional spillover of tourism development between cities, is crucial for the future sustainable and coordinated development of tourism in the BTHUA.

Intraday liquidity risk: based on microprudential and macroprudential regulatory perspective

ABSTRACT This paper addresses the effectiveness of current regulations on intraday liquidity risk through a comprehensive analysis using network structure and payment flow characteristics of China’s large-value payment system. Utilizing simulation and econometric methods, it examines the factors influencing the upper limit of liquidity levels required by payment system participants to satisfy real-time gross settlement (RTGS) and its tail distribution. The results of the study show that: (1) in a network topology, the characteristics of a node significantly affect its demand for intraday liquidity and its exposure to intraday liquidity risk; (2) microprudential regulation based only on individuals not only fails to achieve macroprudential regulatory objectives, but also fails to realize individual microprudential regulatory objectives, and at the same time, it reduces the efficiency of liquidity use; (3) reallocating intraday liquidity among participants based on systemic effects not only saves liquidity overall, but also achieves the macroprudential regulatory objective of liquidity at a high probability level. This study provides valuable theoretical and practical insights for regulators and financial managers, emphasizing the importance of effective regulation to prevent the evolution of intraday liquidity risk into systemic risk.

WITHDRAWN: Restoration of the ecological network structure based on the spatiotemporal changes of the wintering Grus japonensis habitat in the Yancheng coastal area

As an Asian endemic species, Grus japonensis (G. japonensis) reproduction and survival rely on natural wetland resources. G. japonensis is adept at long-distance flight, thereby displaying distinct network structural characteristics in its utilization of habitat within its range. Therefore, scientific restoration and management of the ecological network (ENs) are urgently needed. On the basis of the data of G. japonensis and environmental data, this study used the MaxEnt model, stepping-stone theory, and circuit models to analyze the spatiotemporal changes in the ENs structure of G. japonensis habitat. The primary factors influencing the habitat suitability of G. japonensis was the distance to Phragmites australis (P. australis) community. The area of ecological source areas decreased from 56,528.59 hm2 in 1987 to 14,040.20 hm2 in 2021, with the distribution range contracting from Beihuan (BH), Nanhuan (NH), Hexin (HX) and Tiaozini (TZN) to Hexin (HX). Between 1987 and 2007, there were dense ecological corridors along the coastal areas. However, by 2021, only BH and HX had dense ecological corridors between them. The node centrality of HX decreased from 900.78 in 1987 to 264.64 in 2021, with the most significant decrease between 2007 and 2021. On this basis, the expanded habitat area, the stepping stone area in 2021 and the ecological source area of 2007 with low development intensity were taken as restoration areas. The restoration areas were classified into two types based on their distribution: landing nodes and expansion nodes. The addition of landing nodes enhanced the overall stability of the ENs, resulting in an increase in the centrality of nodes from 264.64 to 398.08. Finally, an optimization and restoration framework for the habitat network based on identifying priority restoration areas, land use type transformation, and enhancing ENs stability for habitat restoration was proposed.

Abnormal cascading dynamics in transportation networks based on Gaussian distribution of load

In the transportation network, we observe that the distance people travel by means of transportation follows a certain distribution. Statistical analysis shows that people’s travel distance is mainly concentrated in a medium range by the same vehicle, and they choose fewer destinations that are extremely close or far away. However, in previous studies, the impact of distance on the distribution of load flow within the network has often been neglected, or at best, addressed with overly simplistic assumptions. Therefore, we quantify the load flow distribution based on the Gaussian distribution of distances between the nodes. On this basis, a new cascading failure model is proposed using the shortest path strategy to calculate the initial load of the edge. Through the simulation of three real traffic networks and two artificially constructed networks with similar structural characteristics of traffic networks, we found the following interesting anomalies: First, increasing the load-bearing capacity of edges within the network does not necessarily lead to enhanced robustness. Second, we observed that removing more edges does not necessarily lead to a decrease in network robustness; conversely, the network robustness can be higher when a moderate number of edges are removed compared to fewer edges. To better understand the two anomalous dynamics phenomena we observed, we ran simulations on a small-scale network extracted from a real traffic network. We found that, under certain circumstances, the premature failure of some edges may isolate certain regions from the network, which may be responsible for this paradox.

Roles of network topology in the relaxation dynamics of simple chemical reaction network models

Understanding the relationship between the structure of chemical reaction networks and their reaction dynamics is essential for unveiling the design principles of living organisms. However, while some network-structural features are known to relate to the steady-state characteristics of chemical reaction networks, mathematical frameworks describing the links between out-of-steady-state dynamics and network structure are still underdeveloped. Here, we characterize the out-of-steady-state behavior of a class of artificial chemical reaction networks consisting of the ligation and splitting reactions of polymers. Within this class, we examine minimal networks that can convert a given set of sources (e.g., nutrients) to a specified set of targets (e.g., biomass precursors). By exploring the dynamics of the models with a simple setup, we find three distinct types of relaxation dynamics after perturbation from a steady-state: exponential-, power-law-, and plateau-dominated. We computationally show that we can predict this out-of-steady-state dynamical behavior from just three features computed from the network’s stoichiometric matrix, namely, (1) the rank gap, determining the existence of a steady-state; (2) the left null-space, being related to conserved quantities in the dynamics; and (3) the stoichiometric cone, dictating the range of achievable chemical concentrations. We further demonstrate that these three quantities relates to the type of relaxation dynamics of combinations of our minimal networks, larger networks with many redundant pathways, and a real example of a metabolic network. The relationship between the topological features of reaction networks and the relaxation dynamics presented here are useful clues for understanding the design of metabolic reaction networks as well as industrially useful chemical production pathways.

Source localization in signed networks based on dynamic message passing algorithm

Sign characteristics of social networks play a crucial role in the process of information dissemination, and structural balance theory is a significant property of signed networks. Despite some progress in source localization, the sign features of network structures are widely overlooked. Furthermore, the impact of structural balance on tracing remains unclear. In this paper, we explore a model of information propagation in signed networks and investigate source localization in the context of a given network state snapshot. We first propose a modified signed susceptibility–infection–recovery (S-SIR) propagation model, combining the positive/negative transmission rates based on structural balance theory, which is designed to better approximate the real-world social network structure. Next, a signed dynamic message passing algorithm (S-DMP) is proposed for source localization. Simulation experiments show that the S-DMP algorithm can fully utilize both the positive and negative attributes of the edges in the signed network and outperforms other methods in terms of source localization accuracy. These experiments prove that the proposed method can be used for source localization in the early stage of information transmission. Furthermore, we find that the accuracy of source localization is highest when there is a balance between the number of positive and negative edges.

Urban Internal Network Structure and Resilience Characteristics from the Perspective of Population Mobility: A Case Study of Nanjing, China

In the face of diverse chronic pressures and increased factor mobility, the resilience of urban internal network structures has become a cutting-edge research topic. This study utilizes 2019 mobile signaling big data to construct employment and recreational flow networks among 101 townships and streets within Nanjing City. Based on the characteristics of these network structures, the resilience of the network structure is measured from the perspectives of density, symmetry, and transmissibility through interruption simulation techniques. The results show that the intensity of population mobility within Nanjing presents a general decay from the central urban area to the outer layers. In the employment scenario, cross-river population mobility is more frequent, while in the recreational scenario, the natural barrier effect of the Yangtze River is prominent. Due to the concentration of employment centers and high spatial heterogeneity, the employment flow network exhibits greater vulnerability to sudden shocks. Townships and streets with weighted degree values ranking around 60 and 80 are of great importance for maintaining the normal operation of both employment and recreational flow networks. Strengthening the construction of resilient parks and village planning within resilient cities can enhance the risk resistance of employment and recreational flow networks.

A learning-based influence maximization framework for complex networks via K-core hierarchies and reinforcement learning

Influence maximization (IM) is a critical aspect of complex network analysis, as it holds considerable commercial value across various domains such as recommendation systems and viral marketing. Its objective is to identify a set of seed nodes that can initiate the most extensive cascade of influence within the network. However, IM encounters significant challenges due to its NP-hard nature of accurately identifying the optimal seed nodes and estimating the spread of influence. Although recent learning-based IM methods have shown promising results, there are still challenges related to scalability, efficiency, and avoiding influence overlap that need to be addressed. This paper proposes CoreQ, a novel reinforcement learning-based framework that leverages K-core hierarchies to address IM challenges and make advised decisions to learn the optimal policy to select seed nodes. CoreQ significantly enhances IM by reducing computational costs and effectively addressing the influence overlap problem, making it a scalable and efficient solution. CoreQ first utilizes K-core decomposition to analyze the network structure and extract hierarchical features that guide the process of seed node identification. Then, it employs a novel maximum likelihood-based approach to identify a sufficient number of candidate seed nodes from K-core hierarchies. Finally, it leverages Q-learning’s decision-making capabilities to optimize a policy for learning the most effective seed selection strategy. The experimental results demonstrate that CoreQ not only outperforms state-of-the-art IM methods in terms of influence spread under the weighted independent cascade model but also surpasses other competing learning-based IM methods in terms of time efficiency.

Comprehensive resilience assessment of bridge networks using ensemble learning method

The assessment of seismic resilience in bridge networks holds significant importance for urban disaster prevention and mitigation efforts. Unlike individual bridges, there has been limited efficiency in assessing bridge networks. A seismic resilience assessment methodology for bridge networks using ensemble learning methods is proposed in this paper. Initially, a comprehensive resilience index is proposed, integrating both structural and functional aspects of bridge networks. Using 3 ensemble learning methods, 9 parameters related to network structure and traffic characteristics are chosen as input variables for predicting the seismic resilience index. Finite element models of 18 bridges are constructed and combined to generate 3500 sets of virtual bridge networks for model training. The predictive accuracy of models trained using the 3 ensemble methods exceeds 89 %, and the expected values of peak ground acceleration (PGA) and functional loss rate are the most influential features. The methodology offers insights into the application of ensemble learning for bridge network seismic resilience assessment.

Diagnostic discrimination of social network indicators in alcohol use disorder: Initial examination using high-resolution and brief assessments.

Social network analysis (SNA) characterizes the structure and composition of a person's social relationships. Network features have been associated with alcohol consumption in observational studies, primarily of university undergraduates. No studies have investigated whether indicators from a person's social network can accurately identify the presence of alcohol use disorder (AUD), offering an indirect strategy for identifying AUD. Two cross-sectional case-control designs examined the clinical utility of social network indicators for identifying individuals with AUD (cases) versus demographically matched drinkers without AUD (controls). Study 1 (N = 174) used high-resolution egocentric SNA assessment, whereas Study 2 (N = 189) used a brief assessment. In Study 1, significant differences between AUD+ participants and controls were present for network alcohol severity (i.e., heavy drinking days; d = 1.23) and frequency (d = 0.35), but not network structural features. Network alcohol severity exhibited very good classification of AUD+ individuals versus controls (area under the curve [AUC] = 0.80), whereas network frequency did not (AUC = 0.61). In Study 2, significant differences were present for network alcohol severity (d = 1.02), quantity (d = 0.74), and frequency (d = 0.43), and severity exhibited good differentiation (AUC = 0.76). Social network indicators of alcohol involvement robustly differentiated AUD+ individuals from matched controls, and the brief assessment performed almost as well as the high-resolution assessment. These findings provide proof-of-concept for severity-related SNA indicators as promising novel clinical assessments for AUD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The destruction of cytoplasmic skeleton leads to the change of nuclear structure and the looseness of lamin A submicroscopic network

The interaction between lamin A and the cytoplasmic skeleton plays a key role in maintaining nuclear mechanical properties. However, the effect of destruction of the cytoplasmic skeleton on the 3D submicroscopic structure of lamin A has not been elucidated. In this study, we developed an image quantization algorithm to quantify changes in the submicroscopic structure of the intact lamin A 3D network within the nucleus. We used blebbistatin or nocodazole to disrupt the fibrillar structure of F-actin or tubulin, respectively, and then quantified changes in the lamin A super-resolution network structure, the morphological and mechanical properties of the nucleus and the spatial distribution of chromosomes. Ultimately, we found for the first time that disruption of the cytoplasmic skeleton changes the lamin A submicroscopic network and nuclear structural characteristics. In summary, this study contributes to understanding the trans-nuclear membrane interaction characteristics of lamin A and the cytoplasmic skeleton.