High Clustering Coefficient Research Articles

Effect of mesoscopic structure on hydro-mechanical properties of fractures

Geothermal extraction usually requires reservoir fracturing to realize economic exploitation and fluid flow in hot-dry rock fractures has become a research hotspot. In the present study, gas flow through shear and tensile granite fractures was investigated to comparatively study effect of fracture mesoscopic structure evolution on fracture hydro-mechanical characteristics under confining stress ranging from 1 to 40 MPa and inlet pressure ranging from 0.005 to 3.6 MPa. Four major findings are reported herein. First, the conductivity of shear fractures is much higher than that of tensile fractures and the ratio of the former to the latter can be up to around 265 under the stress of 40 MPa. The second finding was that the Z2 values of tensile fractures are higher than shear fractures, indicating that the fracture surface of tensile fractures is rougher and has more mesoscopic asperities. When the two surfaces get to contact, tip contact and stress concentration happen, resulting in higher deformation in tensile fractures. The cluster coefficient characterizes fracture contact cluster property. Higher cluster coefficient indicates lower fracture deformation and lower viscous loss. Shear fractures have higher cluster coefficient than that of tensile fractures so shear fractures are weakly deformable. However, high flow velocity and Reynolds number easily occur in shear fractures, so transitional and turbulent flow dominates in shear fractures while laminar and transitional flow in tensile fractures. Third, a mechanical model relates the average imbedded depth to loading stress and lacunarity, a hydraulic aperture model based on void space and lacunarity and a friction factor model based on Reynolds number and lacunarity were built, respectively, based on the experimental data. Then a nonlinear flow model was proposed. The fourth finding was that parametric analysis on the cluster coefficient shows that the higher the cluster coefficient, the lower of fracture deformation is and the higher of hydraulic aperture is. When the cluster coefficient is high, less viscous loss occurs; however, the fluid velocity may be high, so the friction factor will be high since more inertial loss happens.

Correction: Complex interaction networks of cytokines after transarterial chemotherapy in patients with hepatocellular carcinoma

[This corrects the article DOI: 10.1371/journal.pone.0224318.].

A Pragmatic Approach to Clear Channel Assessment Threshold Adaptation and Transmission Power Control for Performance Gain in CSMA/CA WLANs

We propose practical sets of rules for adapting Clear Channel Assessment (CCA) and Transmit Power (TP) parameters by simulating ensembles of randomly generated wireless networks and collecting throughput statistics. The rules’ performances depend strongly on network topology, with increases in throughput in many cases. However, networks with a high clustering coefficient are often adversely effected by the adaptations. But simulations of small-scale networks show that apparently adverse adaptations may still yield benefits for uneven demand combinations. Finally, we have found that throughput is not usually correlated with the number of hidden or exposed nodes in any non-trivial network set-up.

Systemic risk assessment through high order clustering coefficient

In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the nodes of the network. The proposed measure incorporates the generalized concept of clustering coefficient of order $l$ of a node $i$ introduced in Cerqueti et al. (2018). Its properties are also explored in terms of systemic risk assessment. Empirical experiments on the time-varying global banking network show the effectiveness of the presented systemic risk measure and provide insights on how systemic risk has changed over the last years, also in the light of the recent financial crisis and the subsequent more stringent regulation for globally systemically important banks.

Pig trade networks through live pig markets in Guangdong Province, China.

This study used social network analysis to investigate the indirect contact network between counties through the movement of live pigs through four wholesale live pig markets in Guangdong Province, China. All 14,118 trade records for January and June 2016 were collected from the markets and the patterns of pig trade in these markets analysed. Maps were developed to show the movement pathways. Evaluating the network between source counties was the primary objective of this study. A 1‐mode network was developed. Characteristics of the trading network were explored, and the degree, betweenness and closeness were calculated for each source county. Models were developed to compare the impacts of different disease control strategies on the potential magnitude of an epidemic spreading through this network. The results show that pigs from 151 counties were delivered to the four wholesale live pig markets in January and/or June 2016. More batches (truckloads of pigs sourced from one or more piggeries) were traded in these markets in January (8,001) than in June 2016 (6,117). The pigs were predominantly sourced from counties inside Guangdong Province (90%), along with counties in Hunan, Guangxi, Jiangxi, Fujian and Henan provinces. The major source counties (46 in total) contributed 94% of the total batches during the two‐month study period. Pigs were sourced from piggeries located 10 to 1,417 km from the markets. The distribution of the nodes' degrees in both January and June indicates a free‐scale network property, and the network in January had a higher clustering coefficient (0.54 vs. 0.39) and a shorter average pathway length (1.91 vs. 2.06) than that in June. The most connected counties of the network were in the central, northern and western regions of Guangdong Province. Compared with randomly removing counties from the network, eliminating counties with higher betweenness, degree or closeness resulted in a greater reduction of the magnitude of a potential epidemic. The findings of this study can be used to inform targeted control interventions for disease spread through this live pig market trade network in south China.

Quantitative Controllability Analysis of IEEE Power Network

The previous research on the controllability of complex networks is qualitative issue which can only identify whether the network is controllable or not, and it is difficult to assess the controlled ability of network. In this paper, a quantitative measurement index of network controllability is introduced. And the index is used to study the controllability of the IEEE power network system. The relationship between controllability and network parameters (including the average degree, the clustering coefficient, the average path length) of power network is analyzed by comparing with the ER random network, WS small world network, BA scale-free network and CM network. The results show that the power networks have a small average path length and a higher clustering coefficient, which belong to the small world network. And, the controllability of power networks is larger than the corresponding model networks, indicating that power networks are relatively easier to be controlled.

Study of dynamic characteristics of scale-free spiking neural networks based on synaptic plasticity

Biological neural networks have dual properties of small-world attributes and scale-free attributes. Most of the current researches on neural networks are based on small-world networks or scale-free networks with lower clustering coefficient, however, the real brain network is a scale-free network with small-world attributes. In this paper, a scale-free spiking neural network with high clustering coefficient and small-world attribute was constructed. The dynamic evolution process was analyzed from three aspects: synaptic regulation process, firing characteristics and complex network characteristics. The experimental results show that, as time goes by, the synaptic strength gradually decreases and tends to be stable. As a result, the connection strength of the network decreases and tends to be stable; the firing rate of neurons gradually decreases and tends to be stable, and the synchronization becomes worse; the local information transmission efficiency is stable, the global information transmission efficiency is reduced and tends to be stable, and the small-world attributes are relatively stable. The dynamic characteristics vary with time and interact with each other. The regulation of synapses is based on the firing time of neurons, and the regulation of synapses will affect the firing of neurons and complex characteristics of networks. In this paper, a scale-free spiking neural network was constructed, which has biological authenticity. It lays a foundation for the research of artificial neural network and its engineering application.

A New Small-World IoT Routing Mechanism Based on Cayley Graphs

An increasing number of low-power Internet of Things (IoT) devices will be widely deployed in the near future. Considering the short-range communication of low-power devices, multihop transmissions will become an important transmission mechanism in IoT networks. It is a crucial for low-power devices to transmit data over long distances via multihop in a low-delay and reliable way. The small-world characteristics of networks indicate that the network has an advantage of a small average shortest-path length (ASL) and a high average clustering coefficient (ACC). In this article, a new IoT routing mechanism considering small-world characteristics is proposed to reduce the delay and improve the reliability. The ASL and ACC are derived for the performance analysis of small-world characteristics in IoT networks based on Cayley graphs. Besides, the reliability and delay models are proposed for small-world IoT based on Cayley graphs (SWITCH). The simulation results demonstrate that SWITCH has lower delay and better reliability than that of conventional nearest neighboring routing (NNR). Moreover, the maximum delay of SWITCH is reduced by 50.6% compared with that by NNR.

Complex interaction networks of cytokines after transarterial chemotherapy in patients with hepatocellular carcinoma.

Treating hepatocellular carcinoma with transarterial chemoembolization (TACE) induces both local inflammation in the tumor microenvironment as well as systemic inflammation. We analyzed serum cytokine response to TACE to evaluate this. Serum samples obtained from 203 HCC patients treated with TACE were analyzed for inflammatory cytokines including interleukin (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12, IL-13, IL-17, IL-22, TNF-α, IFN-γ, and C-reactive protein (CRP) levels. Cytokine concentrations were measured at day 0 (D0, baseline, n = 203), day3 (D3, n = 156), day7 (D7, n = 147), and day 60 (D60, n = 115) after TACE. Network analysis of the cytokines was performed to understand their interactive relationship. After TACE, IL-1β, -6,-9, -12, and -22 increased by D60. IL-2, -5, -10, -17A and INF-γ decreased by D60, and IL-4, -13 and TNF-α revealed stable concentration. D0 network revealed that IL-2, -4, -5, and -10 formed a module. D3 network had the highest clustering coefficient and average degree that revealed similar pattern as CRP. D7 network revealed that IL-6, -9 and CRP were isolated from the network. D60 network had the lower network heterogeneity and lower clustering coefficient, network diameter, shortest path and characteristic path length. Degree correlation revealed that assortative network turned to disassortative network by D60 indicating that the network gained scale free feature. D60 cytokine network retained inflammatory function and these parameters indicated that the systemic inflammation induced by TACE appeared to be attenuated by D60. IL-9 at D3 and D7 seemed to be related to anti-tumor effect and IL-6 at D7 and D60, and IL-22 at D60 was related to regenerative but not pro- or anti- inflammatory function. Median survival month of patient group with high and low values of cytokine with P-values were as follows: D0 CRP, 9.5 and 54.2 months (P<0.0001); D0 IL-2, 39.9 and 56.1 months (P = 0.0084); D3 CRP, 31.3 and 55.1 months (P = 0.0056); D7 CRP, 28.7 and 50.7 months (P = 0.0065), respectively. TACE is associated with systemic inflammation which appears to peak at Day 3 and resolve by D60. Among the tested cytokines, IL-6 and IL-22 appear to play a regenerative role.

Automatic generation of adaptive network models based on similarity to the desired complex network

Complex networks have become powerful mechanisms for studying a variety of real-world systems. Consequently, many human-designed network models are proposed that reproduce nontrivial properties of complex networks, such as long-tail degree distribution or high clustering coefficient. Therefore, we may utilize network models in order to generate graphs similar to desired networks. However, a desired network structure may deviate from emerging structure of any generative model, because no selected single model may support all the needed properties of the target graph and instead, each network model reflects a subset of the required features. In contrast to the classical approach of network modeling, an appropriate modern network model should adapt the desired features of the target network. In this paper, we propose an automatic approach for constructing network models that are adaptive to the desired network features. We employ Genetic Algorithms in order to evolve network models based on the characteristics of the target networks. The experimental evaluations show that our proposed framework, called NetMix, results network models that outperform state-of-the-art baseline models according to the compliance with the desired features of the target networks.

Network Evolution of a Large Online MSM Dating Community: 2005–2018

Due to multiple sexual partners and low rates of condom use, the HIV infection rate among MSM (men who have sex with men) is much higher than that of the general population. In order to analyze the characteristics of online activities of MSM, and to understand the evolution of their social networks, in this study we collect a comprehensive dataset, covering the period from January 2005 to June 2018, from the largest Chinese online community, Baidu Tieba. We build an online dating network for MSM-related individuals in the gay-bar community, and analyze the network from static and dynamic aspects. It is found that there is a strong homophily regarding the cities where users reside when developing interactions with others, and that most network measurements tend to be stable at the later stages of evolution, while the size of the largest community fluctuates. This is an indication that the network is formed of rapidly flexible interactions which changes quickly. In comparison with studies on heterosexual networks, we find that the MSM dating network shows differences in many aspects, such as the positive degree-degree correlation and high clustering coefficient, suggesting different thinking and measures should be taken in the policy making of public health management towards the MSM population.

Highly clustered complex networks in the configuration model: Random regular small-world network

We propose a method to make a highly clustered complex network within the configuration model. Using this method, we generated highly clustered random regular networks and analyzed their properties. We show that highly clustered random regular networks with appropriate parameters satisfy all the conditions of the small-world network: connectedness, high clustering coefficient, and small-world effect. We also study how clustering affects the percolation threshold in random regular networks. In addition, the prisoner's dilemma game is studied and the effects of clustering and degree heterogeneity on the cooperation level are discussed.

A two-stage BFS local community detection algorithm based on node transfer similarity and Local Clustering Coefficient

Local community detection is of great value to network analysis, because it can efficiently find the community in which the given node located. However, the seed-dependent, core-criteria and termination problems are still difficulties for the major detection frames, which explore the core community and extend the community based on evaluation functions. In this paper, we proposed a local community detection algorithm (TSB) based on Breadth First Search (BFS), the proposed node transfer similarity and Local Clustering Coefficient (LCC). In our modeling, the initial clustering ability is strengthened by clustering the direct neighbor node with high clustering coefficient, so the seed-dependent problem could be avoided. We gave a new core criteria to evaluate the candidate core nodes, which is combined of LCC, the node transfer similarity and BFS depth. The dynamic termination thresholds for each candidate node in the core and extension stages are separately relying on its father node and the average node transfer similarity. In the experimental work, we compared our method with several previous methods through real-world and artificial synthetic networks. The results show that our method has better performance on accuracy with various community structure.

Resting-State Stereotactic Electroencephalography May Help Localize Epileptogenic Brain Regions

Abstract INTRODUCTION Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy, but requires days in the hospital with interventions to trigger several uncomfortable seizures. Our goal is to make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS Compared to nonepileptogenic structures, we found increased connectivity within epileptogenic regions (P &lt; .05) and between epileptogenic areas and other structures (P &lt; .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P &lt; .01) and betweenness centrality (P &lt; .01), and greater decay of connectivity with distance (P &lt; .05, paired t-tests, corrected). Our connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve (AUC) of 0.78 and accuracy of 80.4%. CONCLUSION Our study represents a preliminary step towards defining resting-state SEEG connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

A novel evolving model of urban rail transit networks based on the local-world theory

Since the expansion of the scale of urban rail construction, the networked structure is becoming a significant characteristic of rail transit systems. For a further understanding of the networked structure, the evolution mechanism of the rail transit network is worth research and discussion. To select the most appropriate modeling space, four topological spaces (L-Space, P-Space, B-Space, and R-Space) are analyzed based on three indicators (degree distribution, clustering coefficient, and average length); P-space is selected as the basic space for topology because of its high clustering coefficient and low average length. The topological network, which is obtained by P-space, shows exponential degree distribution and local-world characteristics. After dissecting the evolution law of degree distribution and other parameters and the connection mechanism of rail transit topology, the improved local-world evolving model of urban rail transit networks (URTNs) is developed. The model is verified by its application in six cities’ rail-transit networks (London, New York, Paris, Beijing, Shanghai, and Shenzhen). The results show little difference between the real network and evolving network, with a high consistency of their degree distributions and the network indicators. These illustrate that the model can reflect the real characteristics of URTNs and that it can be used to generate a new network that has a similar structure to the real network.

Evaluating link significance in maintaining network connectivity based on link prediction

Evaluating the significance of nodes or links has always been an important issue in complex networks, and the definition of significance varies with different perspectives. The significance of nodes or links in maintaining the network connectivity is widely discussed due to its application in targeted attacks and immunization. In this paper, inspired by the weak tie phenomenon, we define the links' significance by the dissimilarity of their endpoints. Some link prediction algorithms are introduced to define the dissimilarity of nodes based solely on the network topology. Experiments in synthetic and real networks demonstrate that the method is especially effective in the networks with higher clustering coefficients.

Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls.

The human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial enzymes carrying out essential physio-biochemical reactions. Thus, the changes of essential microbial enzyme levels may predict human disorders. With the rapid development of high-throughput sequencing technologies, metagenomics analysis has emerged as an important method to explore the microbial communities in the human body, as well as their functionalities. In this study, we analyzed 156 gut metagenomics samples from patients with colorectal cancer (CRC) and adenoma, as well as that from healthy controls. We estimated the abundance of microbial enzymes using the HMP Unified Metabolic Analysis Network method and identified the differentially abundant enzymes between CRCs and controls. We constructed enzymatic association networks using the extended local similarity analysis algorithm. We identified CRC-associated enzymic changes by analyzing the topological features of the enzymatic association networks, including the clustering coefficient, the betweenness centrality, and the closeness centrality of network nodes. The network topology of enzymatic association network exhibited a difference between the healthy and the CRC environments. The ABC (ATP binding cassette) transporter and small subunit ribosomal protein S19 enzymes, had the highest clustering coefficient in the healthy enzymatic networks. In contrast, the Adenosylhomocysteinase enzyme had the highest clustering coefficient in the CRC enzymatic networks. These enzymic and metabolic differences may serve as risk predictors for CRCs and are worthy of further research.

On the maximum small-world subgraph problem

A clique (or a complete subgraph) is a popular model for an “ideal” cluster in a network. However, in many practical applications this notion turns out to be overly restrictive as it requires the existence of all pairwise links within the cluster. Thus, the researchers and practitioners often rely on various clique relaxation ideas for more flexible models of highly connected clusters. In this paper, we propose a new clique relaxation model referred to as a small-world subgraph, which represents a network cluster with “small-world” properties: low average distance and high clustering coefficient. In particular, we demonstrate that the proposed small-world subgraph model has better “cohesiveness” characteristics than other existing clique relaxation models in some worst-case scenarios. The main focus of the paper is on the problem of finding a small-world subgraph of maximum cardinality in a given graph. We describe a mixed integer programming (MIP) formulation of the problem along with several algorithmic enhancements. For solving large-scale instances of the problem we propose a greedy-type heuristic referred to as the iterative depth-first search (IDF) algorithm. Furthermore, we show that the small-world subgraphs identified by the IDF algorithm have an additional property that may be attractive from the practical perspective, namely, 2-connectivity. Finally, we perform extensive computational experiments on real-world and randomly generated networks to demonstrate the performance of the developed computational approaches that also reveal interesting insights about the proposed clique relaxation model.

Critical excitation-inhibition balance in dense neural networks

The "edge of chaos" phase transition in artificial neural networks is of renewed interest in light of recent evidence for criticality in brain dynamics. Statistical mechanics traditionally studied this transition with connectivity k as the control parameter and an exactly balanced excitation-inhibition ratio. While critical connectivity has been found to be low in these model systems, typically around k=2, which is unrealistic for natural neural systems, a recent study utilizing the excitation-inhibition ratio as the control parameter found a new, nearly degree independent, critical point when connectivity is large. However, the new phase transition is accompanied by an unnaturally high level of activity in the network. Here we study random neural networks with the additional properties of (i) a high clustering coefficient and (ii) neurons that are solely either excitatory or inhibitory, a prominent property of natural neurons. As a result, we observe an additional critical point for networks with large connectivity, regardless of degree distribution, which exhibits low activity levels that compare well with neuronal brain networks.

Cortical Functional Connectivity during Praxis in Autism Spectrum Disorder.

Abnormal functional connectivity was reported as one of the underlying characteristics of autism spectrum disorder (ASD). Considering the motor deficits in ASD, we utilized praxis to investigate the neural mechanisms of ASD during motor task. Since the previous functional connectivity studies reported divergent results, we explored the properties of the functional connectivity using graph metrics to address brain organization alterations of ASD. We proposed the use of eLORETA to investigate the cortical connectivity during praxis based on a cohort of 45 high-functioning ASD (HFA) children and 45 typically developing (TD) children. The between-group comparison revealed higher clustering coefficient and lower global efficiency for HFA relative to TD while the between-phase comparison suggested decreasing global efficiency, increasing characteristic path length for TD. Nodal metrics exhibited significant differences between groups in frontal and occipital regions. These regions also showed significant changes of nodal metrics and connection strengths between baseline and praxis execution for TD. However, there were no significant changes in global, nodal metrics and connection strengths between phases for HFA. Our study suggested that cortical connectivity in ASD exhibited lower overall efficiency and a deficit in reorganization, which deepens the understanding of abnormal brain organization in ASD.