Characteristics Of Complex Networks Research Articles

Generating Intra and Inter-provincial Commercial Vehicle Activity Chains

Modelling large-scale traffic flow systems at a disaggregate level can be data intensive as it requires extensive knowledge about the activities and activity chains of vehicles. This paper focuses on activity chain generation for commercial vehicles. We use a large sample of GPS records to extract a complex network and sample chain characteristics from. The paper makes a valuable contribution in both its methodology, and in its focus on intra and inter-provincial vehicle populations simultaneously. The simulated chains are validated in terms of vehicle kilometrekilometres travelled and its spatiotemporal accuracy, comparing favorably in both.

Nonparametric resampling of random walks for spectral network clustering

Parametric resampling schemes have been recently introduced in complex network analysis with the aim of assessing the statistical significance of graph clustering and the robustness of community partitions. We propose here a method to replicate structural features of complex networks based on the non-parametric resampling of the transition matrix associated with an unbiased random walk on the graph. We test this bootstrapping technique on synthetic and real-world modular networks and we show that the ensemble of replicates obtained through resampling can be used to improve the performance of standard spectral algorithms for community detection.

Study of brain functional network based on sample entropy of EEG under magnetic stimulation at PC6 acupoint

Acupuncture is based on the theory of traditional Chinese medicine. Its therapeutic effectiveness has been proved by clinical practice. However, its mechanism of action is still unclear. Magnetic stimulation at acupuncture point provides a new means for studying the theory of acupuncture. Based on the Graph Theory, the construction and analysis method of complex network can help to investigate the topology of brain functional network and understand the working mechanism of brain. In this study, magnetic stimulation was used to stimulate Neiguan (PC6) acupoint and the EEG (Electroencephalograph) signal was recorded. Using non-linear method (Sample Entropy) and complex network theory, brain functional network based on EEG signal under magnetic stimulation at PC6 acupoint was constructed and analyzed. In addition, the features of complex network were comparatively analyzed between the quiescent and stimulated states. Our experimental results show the topology of the network is changed, the connection of the network is enhanced, the efficiency of information transmission is improved and the small-world property is strengthened through stimulating the PC6 acupoint.

An Efficient Hierarchy Algorithm for Community Detection in Complex Networks

Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm—CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithm is distributed and polynomial; meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too.

Co-expression analysis of fetal weight-related genes in ovine skeletal muscle during mid and late fetal development stages.

Background: Muscle development and lipid metabolism play important roles during fetal development stages. The commercial Texel sheep are more muscular than the indigenous Ujumqin sheep.Results: We performed serial transcriptomics assays and systems biology analyses to investigate the dynamics of gene expression changes associated with fetal longissimus muscles during different fetal stages in two sheep breeds. Totally, we identified 1472 differentially expressed genes during various fetal stages using time-series expression analysis. A systems biology approach, weighted gene co-expression network analysis (WGCNA), was used to detect modules of correlated genes among these 1472 genes. Dramatically different gene modules were identified in four merged datasets, corresponding to the mid fetal stage in Texel and Ujumqin sheep, the late fetal stage in Texel and Ujumqin sheep, respectively. We further detected gene modules significantly correlated with fetal weight, and constructed networks and pathways using genes with high significances. In these gene modules, we identified genes like TADA3, LMNB1, TGF-β3, EEF1A2, FGFR1, MYOZ1, and FBP2 correlated with fetal weight.Conclusion: Our study revealed the complex network characteristics involved in muscle development and lipid metabolism during fetal development stages. Diverse patterns of the network connections observed between breeds and fetal stages could involve some hub genes, which play central roles in fetal development, correlating with fetal weight. Our findings could provide potential valuable biomarkers for selection of body weight-related traits in sheep and other livestock.

A New MANET Accessing Internet Method Based on Complex Network

Considering the complex network characteristics of mobile ad hoc network, a new MANET accessing Internet model was proposed. In this model, transmit power was adjusted by residual energy of the node, and added long-range edge in network to make MANET with more obvious small world characteristics. On this basis, the paper proposed a gateway programming model, which introduced a decision node to harmonize the utilization of gateways and routes. The model, as the basis of the gateway configuration, optimized the placement of the gateway. The simulation results indicate that the performance of delay, overhead and throughput has promoted after the model was introduced, which validates the validity of the model.

Information Dissemination and Control Models in Complex Network

In order to well understand and control the information dissemination in the complex network, Increasing Trust SI model and SIP model have been built to represent the peoples' interaction during information dissemination. By introducing and computing the trust degree of the net nodes, the process of information dissemination can be truly represented following increasing trust rule. Simulation experiments reveal that the informations can be quickly broken out in ER model, NW model and BA model due to the small world characteristic of complex networks. To prevent bad information disseminating, SIP model with Persuasion mechanism is presented. It can be effect when immune nodes with P status persuade their neighber disseminator with the truth. Simulation experiments show that not all nodes in the network will be disseminated and all will lose the trust on the bad information. Exceptionally, the best effection is in BA network with free-scale. The results of this paper can be used for analysis and management of information dissemination on complex networks. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3662

Controllability in Cancer Metabolic Networks According to Drug Targets as Driver Nodes

Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

The Complex Network of Architectural Design

We all know that the design thinking in architecture design is quite complex. However, the most existing studies on design thinking have been based upon reductionism. By introducing complex network theory, complexity perspective is used to re-interpret the architectural design in this paper. Moreover, the characteristics, rules and thinking characteristics of architectural creation are revealed by the characteristics of complex network. The paper advocates combining holism and reductionism to guide the architectural design, and pointed out that synesthesia exists among different factors of complex network. Based upon the scientific analysis on several famous cases, the author pointed out the great importance of synesthesia in architects' design thinking.

Research of Enterprise Project Chain Risk Element Transmission Based on Complex Network Model

In order to research of enterprise project chain risk element transmission, constructed the enterprise project chain network model based on a complex network and analyzed the complex network characteristics. Take the mean-field theory to analyze the project chain risk element transmission and get the critical value of project chain transmission. The simulation results show that the project chain has the typical characteristics of a complex network. The distribution of it obeys the power law with lower average path length and higher clustering coefficient. It is a typical scale-free network. Based on randomly selecting risk nodes and the key risk nodes, simulate the relationship between transfer rate and the density of project chain risk nodes. It can provide reference for enterprise to make decision of project chain risk prevention. Finally, improve the whole project chain risk management efficiency and valuably.

An efficient agent-based algorithm for overlapping community detection using nodes’ closeness

Communities are groups of nodes forming tightly connected units in networks. Some nodes can be shared between different communities of a network. The presence of overlapping nodes and their associated membership diversity is a common characteristic of social networks. Analyzing these overlapping structures can reveal valuable information about the intrinsic features of realistic complex networks, especially social networks.In this paper, we propose a novel algorithm that is able to detect overlapping and non-overlapping community structures in complex networks. This algorithm uses a number of agents for investigation of the input network. These agents consider different nodes’ closeness in their investigations. Various experiments are carried out on both synthetic and real-world networks that prove that the proposed algorithm outperforms most state-of-the-art algorithms of this field both in terms of the accuracy and execution time.

A Measurement Framework for Directed Networks

Partially-observed network data collected by link-tracing based sampling methods is often being studied to obtain the characteristics of a large complex network. However, little attention has been paid to sampling from directed networks such as WWW and Peer-to-Peer networks. In this paper, we propose a novel two-step (sampling/estimation) framework to measure nodal characteristics which can be defined by an average target function in an arbitrary directed network. To this end, we propose a personalized PageRank-based algorithm to visit and sample nodes. This algorithm only uses already visited nodes as local information without any prior knowledge about the latent structure of the network. Moreover, we introduce a new estimator based on the approximate importance sampling to estimate average target functions. The proposed estimator utilizes calculated PageRank value of each sampled node as an approximation for the exact visiting probability. To the best of our knowledge, this is the first study on correcting the bias of a sampling method by re-weighting of measured values that considers the effect of approximation of visiting probabilities. Comprehensive theoretical and empirical analysis of the estimator demonstrate that it is asymptotically unbiased even in situations where stationary distribution of PageRank is poorly approximated.

ZERO NODES EFFECT: VALID LINK PREDICTION IN SPARSE NETWORKS

Zero-degree nodes are an important difficulty in sparse networks' link prediction. Clustering and preferential attachment, as the most important characteristics of complex networks, have been paid little attention in similarity indices. Inspired by the coexistence of clustering and preferential attachment in real networks, this paper proposes a new preferential attachment index and new clustering index, which have here been integrated into a hybrid index that considers the dynamic evolutionary forces of complex networks and can solve the problem of excessive zero-degree nodes in sparse networks and check evolution mechanism. Experiments proved prediction accuracy can be remarkably enhanced.

Dynamical correlation patterns and corresponding community structure in neural spontaneous activity at criticality.

It has been considered that the state in the vicinity of a critical point, which is the point between ordered and disordered states, can underlie and facilitate information processing of the brain in various aspects. In this research, we numerically study the influence of criticality on one aspect of brain information processing, i.e., the community structure, which is an important characteristic of complex networks. We examine community structure of the functional connectivity in simulated brain spontaneous activity, which is based on dynamical correlations between neural activity patterns at different positions. The brain spontaneous activity is simulated by a neural field model whose parameter covers subcritical, critical, and supercritical regions. Then, the corresponding dynamical correlation patterns and community structure are compared. In the critical region, we found some distinctive properties, namely high correlation and correlation switching, high modularity and a low number of modules, high stability of the dynamical functional connectivity, and moderate flexibility of the community structure across temporal scales. We also discuss how these characteristics might improve information processing of the brain.

Emergence of network features from multiplexity.

Many biological and man-made networked systems are characterized by the simultaneous presence of different sub-networks organized in separate layers, with links and nodes of qualitatively different types. While during the past few years theoretical studies have examined a variety of structural features of complex networks, the outstanding question is whether such features are characterizing all single layers, or rather emerge as a result of coarse-graining, i.e. when going from the multilayered to the aggregate network representation. Here we address this issue with the help of real data. We analyze the structural properties of an intrinsically multilayered real network, the European Air Transportation Multiplex Network in which each commercial airline defines a network layer. We examine how several structural measures evolve as layers are progressively merged together. In particular, we discuss how the topology of each layer affects the emergence of structural properties in the aggregate network.

Neural networks in psychiatry

Over the past three decades numerous imaging studies have revealed structural and functional brain abnormalities in patients with neuropsychiatric diseases. These structural and functional brain changes are frequently found in multiple, discrete brain areas and may include frontal, temporal, parietal and occipital cortices as well as subcortical brain areas. However, while the structural and functional brain changes in patients are found in anatomically separated areas, these are connected through (long distance) fibers, together forming networks. Thus, instead of representing separate (patho)-physiological entities, these local changes in the brains of patients with psychiatric disorders may in fact represent different parts of the same ‘elephant’, i.e., the (altered) brain network. Recent developments in quantitative analysis of complex networks, based largely on graph theory, have revealed that the brain's structure and functions have features of complex networks. Here we briefly introduce several recent developments in neural network studies relevant for psychiatry, including from the 2013 special issue on Neural Networks in Psychiatry in European Neuropsychopharmacology. We conclude that new insights will be revealed from the neural network approaches to brain imaging in psychiatry that hold the potential to find causes for psychiatric disorders and (preventive) treatments in the future.

1A2-N03 スワームモデルの複雑ネットワークに基づく行動解析(進化・学習とロボティクス(1))

The individual in a swarm emerges controlled behavior by interaction to neighboring individuals. This study observes the swarm consisting of a leader and followers, where the follower are modeled by Boid, and estimates the degree of swarm behavior. Then, the whole network connected by sub-networks, which are made by individuals within a view field of each individual, is analyzed from a viewpoint of the complex network characteristics.

Empirical Tests of Scale-Free Characteristic in Open Source Software: A Replicated Case Study

Open source software systems are becoming increasingly important these days and will grow stronger in the future. In order to gain more information about them, their structure characteristics and behavior characteristics need to be measured. This paper tests scale free characteristics on open source real-world software empirically. More specially, this paper checks whether Chidamber and Kemerer metrics suite follows power law or not through three versions of Weka. Our empirical results showed that the features of complex networks didn’t exist in Weka.

Discriminating Different Classes of Biological Networks by Analyzing the Graphs Spectra Distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a “fingerprint”. Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the “uncertainty” of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.

Beyond random walk and metropolis-hastings samplers

Graph sampling via crawling has been actively considered as a generic and important tool for collecting uniform node samples so as to consistently estimate and uncover various characteristics of complex networks. The so-called simple random walk with re-weighting (SRW-rw) and Metropolis-Hastings (MH) algorithm have been popular in the literature for such unbiased graph sampling. However, an unavoidable downside of their core random walks -- slow diffusion over the space, can cause poor estimation accuracy. In this paper, we propose non-backtracking random walk with re-weighting (NBRW-rw) and MH algorithm with delayed acceptance (MHDA) which are theoretically guaranteed to achieve, at almost no additional cost, not only unbiased graph sampling but also higher efficiency (smaller asymptotic variance of the resulting unbiased estimators) than the SRW-rw and the MH algorithm, respectively. In particular, a remarkable feature of the MHDA is its applicability for any non-uniform node sampling like the MH algorithm, but ensuring better sampling efficiency than the MH algorithm. We also provide simulation results to confirm our theoretical findings.