A New Weighted Degree Centrality Measure: The Application in an Animal Disease Epidemic

In recent years researchers have investigated a growing number of weighted heterogeneous networks, where connections are not merely binary entities, but are proportional to the intensity or capacity of the connections among the various elements. Different degree centrality measures have been proposed for this kind of networks. In this work we propose weighted degree and strength centrality measures (WDC and WSC). Using a reducing factor we correct classical centrality measures (CD) to account for tie weights distribution. The bigger the departure from equal weights distribution, the greater the reduction. These measures are applied to a real network of Italian livestock movements as an example. A simulation model has been developed to predict disease spread into Italian regions according to animal movements and animal population density. Model’s results, expressed as infected regions and number of times a region gets infected, were related to weighted and classical degree centrality measures. WDC and WSC were shown to be more efficient in predicting node’s risk and vulnerability. The proposed measures and their application in an animal network could be used to support surveillance and infection control strategy plans.     

Perturbation Centrality and Turbine: A Novel Centrality Measure Obtained Using a Versatile Network Dynamics Tool

Analysis of network dynamics became a focal point to understand and predict changes of complex systems. Here we introduce Turbine, a generic framework enabling fast simulation of any algorithmically definable dynamics on very large networks. Using a perturbation transmission model inspired by communicating vessels, we define a novel centrality measure: perturbation centrality. Hubs and inter-modular nodes proved to be highly efficient in perturbation propagation. High perturbation centrality nodes of the Met-tRNA synthetase protein structure network were identified as amino acids involved in intra-protein communication by earlier studies. Changes in perturbation centralities of yeast interactome nodes upon various stresses well recapitulated the functional changes of stressed yeast cells. The novelty and usefulness of perturbation centrality was validated in several other model, biological and social networks. The Turbine software and the perturbation centrality measure may provide a large variety of novel options to assess signaling, drug action, environmental and social interventions.     

Attack Robustness and Centrality of Complex Networks

Many complex systems can be described by networks, in which the constituent components are represented by vertices and the connections between the components are represented by edges between the corresponding vertices. A fundamental issue concerning complex networked systems is the robustness of the overall system to the failure of its constituent parts. Since the degree to which a networked system continues to function, as its component parts are degraded, typically depends on the integrity of the underlying network, the question of system robustness can be addressed by analyzing how the network structure changes as vertices are removed. Previous work has considered how the structure of complex networks change as vertices are removed uniformly at random, in decreasing order of their degree, or in decreasing order of their betweenness centrality. Here we extend these studies by investigating the effect on network structure of targeting vertices for removal based on a wider range of non-local measures of potential importance than simply degree or betweenness. We consider the effect of such targeted vertex removal on model networks with different degree distributions, clustering coefficients and assortativity coefficients, and for a variety of empirical networks.     

Structural Controllability and Controlling Centrality of Temporal Networks

Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it''s urgent and significant to focus on its structural controllability as well as the corresponding characteristics, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability as well as its characteristics, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the positive relationship between aggregated degree and controlling centrality as well as the scale-free distribution of node''s controlling centrality are virtually independent of the time scale and types of datasets, meaning the inherent robustness and heterogeneity of the controlling centrality of nodes within temporal networks.     

Towards a Methodology for Validation of Centrality Measures in Complex Networks

Background

Living systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important?

Purpose

The goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets.

Method

We take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary''s Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes.

Results

Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.     

Nodes Having a Major Influence to Break Cooperation Define a Novel Centrality Measure: Game Centrality

Cooperation played a significant role in the self-organization and evolution of living organisms. Both network topology and the initial position of cooperators heavily affect the cooperation of social dilemma games. We developed a novel simulation program package, called ‘NetworGame’, which is able to simulate any type of social dilemma games on any model, or real world networks with any assignment of initial cooperation or defection strategies to network nodes. The ability of initially defecting single nodes to break overall cooperation was called as ‘game centrality’. The efficiency of this measure was verified on well-known social networks, and was extended to ‘protein games’, i.e. the simulation of cooperation between proteins, or their amino acids. Hubs and in particular, party hubs of yeast protein-protein interaction networks had a large influence to convert the cooperation of other nodes to defection. Simulations on methionyl-tRNA synthetase protein structure network indicated an increased influence of nodes belonging to intra-protein signaling pathways on breaking cooperation. The efficiency of single, initially defecting nodes to convert the cooperation of other nodes to defection in social dilemma games may be an important measure to predict the importance of nodes in the integration and regulation of complex systems. Game centrality may help to design more efficient interventions to cellular networks (in forms of drugs), to ecosystems and social networks.     

Control Centrality and Hierarchical Structure in Complex Networks

We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.     

DNA序列信息的一种新的测度

根据信息理论给出了测度ＤＮＡ序列信息的一种新的方法,获得ＤＮＡ序列４个层次的信息量测度：Ｉｂ,Ｉｆ（１）,Ｉｆ（２）ａｎｄＩｆ（３）,这４种信息测度可分别用来测度ＤＮＡ的碱基序列、密码子序列、编码蛋白质序列和功能蛋白质序列的信息量。从Ｍ．ｅｄｕｌｉｓ的线粒体基因组中两个较短的编码蛋白质的ＤＮＡ序列和使用具有不同倍性的间并密码子组组成的模拟ＤＮＡ序列中所获得计算结果表明,这些信息测度确实能用来揭示所     

Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain

Functional magnetic resonance data acquired in a task-absent condition (“resting state”) require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google''s PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular “betweenness centrality” - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.     

Altered Brain Network Centrality in Patients with Diabetic Optic Neuropathy: A Resting-State FMRI Study

Objective: Recent studies have suggested that diabetic optic neuropathy (DON) independently increases the incidence of brain diseases like cerebral infarction and hemorrhage. In this study, voxel-level degree centrality (DC) was used to study potential changes in functional network brain activity in DON patients.Methods: The study included 14 DON patients and 14 healthy controls (HCs) matched by age, sex, and weight. All subjects underwent resting functional magnetic resonance imaging. Receiver operating characteristic curves and Pearson correlation analysis were performed.Results: The DC values of the left frontal mid-orb and right middle frontal gyrus/right frontal sup were significantly lower in DON patients compared to HCs. The DC value of the left temporal lobe was also significantly higher than in HCs.Conclusion: Three different brain regions show DC changes in DON patients, suggesting common optic neuropathy in the context of diabetes and providing new ideas for treating optic nerve disease in patients with long-term diabetes.Abbreviations: AUC = area under the curve; BCVA = best corrected visual acuity; DC = degree centrality; DON = diabetic optic neuropathy; fMRI = functional magnetic resonance imaging; HC = healthy control; LFMO = left frontal mid orb; LTL = left temporal lobe; RFS = right frontal sup; RMFG = right middle frontal gyrus; ROC = receiver operating characteristic     

编者按: 脑成像与脑网络

揭示脑的奥秘是人类面临的最大挑战之一。神经元是构成神经系统结构与功能的基本单位。神经元与神经元之间通过突触实现信息交互,并构成神经环路或神经网络。神经环路有局部的,也有跨脑区或长程的,甚至全脑尺度的。神经环路则是脑实现神经信息处理的基本单元。若干神经环路构成脑网络。脑网络研究已经成为脑功能与脑疾病研究领域的热点。 在国家自然科学基金委员会和科技部“973计划”等项目的支持下,我国科学家在这一领域已经开展了卓有成效的工作。2011年第393次香山科学会议“脑网络组及其临床应用的前沿科学问题”曾对此进行过比较深入的研讨。为促进对该领域现状及发展的了解,本期汇集了2篇述评和2篇研究论文,作为脑成像与脑网络专题发表,以飨读者。 利用9.4T功能磁共振成像(fMRI)获得轻度麻醉状态下大鼠静息状态及刺激激活的数据,通过互相关分析构建节点之间的相关系数矩阵并计算相应的网络参数,赖永秀等人报道了大鼠感觉运动系统静息态脑网络的研究成果,发现感觉运动系统在静息态时的脑网络具有小世界属性。 扩散磁共振成像(dMRI)的出现为大脑结构与功能研究提供了全新的检测手段,雷皓等报道了小动物高分辨扩散磁共振成像数据分析方法,为小动物脑dMRI研究提供了统一图像模板与完善的计算方法,对于检测神经纤维微观结构的变化,以及临床诊断,将具有极其重要的意义。 神经环路功能变化的实时在体监测是研究脑网络不可或缺的手段,曾绍群等评述了基于声光偏转器的快速无惯性随机扫描双光子显微成像技术的研究进展及发展趋势,指出该技术的进一步发展将为神经活动观测提供一种全新的方法,从而极大地推动脑科学研究的发展。 针对哺乳动物全脑的神经元网络成像,龚辉等从空间分辨率、探测范围、数据配准和成像速度等方面评述了光学显微水平全脑成像方法的研究进展,并讨论所面临的挑战。他们指出,要在全脑尺度获取突起水平分辨率的结构与功能数据,光学成像方法最为成熟。华中科技大学研制的MOST系统,率先获得了一系列高分辨率的完整大脑解剖数据集,该成果将在神经元网络的构建和脑功能与疾病研究中发挥重要作用。 我们期待更多、更好的有关脑成像与脑网络的论文发表,以更广泛和深入地促进我国脑科学研究领域的学术交流。     

A Network Centrality Method for the Rating Problem

We propose a new method for aggregating the information of multiple users rating multiple items. Our approach is based on the network relations induced between items by the rating activity of the users. Our method correlates better than the simple average with respect to the original rankings of the users, and besides, it is computationally more efficient than other methods proposed in the literature. Moreover, our method is able to discount the information that would be obtained adding to the system additional users with a systematically biased rating activity.     

Brain Networks Supporting Execution of Mathematical Skills versus Acquisition of New Mathematical Competence

This fMRI study examines how students extend their mathematical competence. Students solved a set of algebra-like problems. These problems included Regular Problems that have a known solution technique and Exception Problems that but did not have a known technique. Two distinct networks of activity were uncovered. There was a Cognitive Network that was mainly active during the solution of problems and showed little difference between Regular Problems and Exception Problems. There was also a Metacognitive Network that was more engaged during a reflection period after the solution and was much more engaged for Exception Problems than Regular Problems. The Cognitive Network overlaps with prefrontal and parietal regions identified in the ACT-R theory of algebra problem solving and regions identified in the triple-code theory as involved in basic mathematical cognition. The Metacognitive Network included angular gyrus, middle temporal gyrus, and anterior prefrontal regions. This network is mainly engaged by the need to modify the solution procedure and not by the difficulty of the problem. Only the Metacognitive Network decreased with practice on the Exception Problems. Activity in the Cognitive Network during the solution of an Exception Problem predicted both success on that problem and future mastery. Activity in the angular gyrus and middle temporal gyrus during feedback on errors predicted future mastery.     

流式原位荧光杂交——测定端粒长度

端粒是染色体末端ＤＮＡ重复序列与特异结合蛋白的复合体。脊椎动物端粒重复序列是 5′ＴＴＡＧＧＧ3′。端粒长度可以作为细胞的“分裂时钟” ,反映细胞的分裂能力。作为染色体末端的帽状结构 ,端粒还有其他生物学功能 :保证染色体完整性 ,使真正的遗传信息得到完整复制 ;保护染色体末端 ,防止染色体异常重组而影响细胞分裂 ;指导染色体与核膜相接。端粒 端粒酶系统对细胞增殖、细胞衰老、细胞永生化、癌变、发育生物学、ＨＩＶ感染的免疫反应、免疫缺陷等有重要意义[1～ 3] 。因此端粒动力学的研究十分重要。1 .ＤＮＡ印迹杂交端粒长度测…     

An Adaptive Complex Network Model for Brain Functional Networks

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.