Complex Network Approach Research Articles

Analysis of Airport Network in Pakistan Utilizing Complex Network Approach

Field of complex network covers different social, technological, biological, scientific collaborative work, communication networks and many others. Among these networks, transportation network is an important indicator to measure the economic growth in any country. In this study different dynamics of Airport Network in Pakistan are analyzed by the complex network methodology. Dataset of air transportation has been collected from Civil Aviation Authority of Pakistan (CAA) and formatted to accomplish the complex network requirements. The network is formed to observe its different properties and compare these with their topological counterparts. In this, network nodes are represented by Airports of Pakistan while flights between them within a week are considered as edges. The behavior of degree distribution is observed as preferential attachment of nodes, which represented that few nodes are controlling overall network which emphasizes that Airport Network in Pakistan (ANP) follows power law. Clustering coefficient displayed the network as a clustered network. Result of short average path length highlights that Airport Network in Pakistan is small-world network. Study also signified the average nearest neighbour degree node, which explained that ANP exhibited disassortative mixing in nature which states that high degree nodes (airports) tend to connect to low degree nodes (airports). Interestingly, is has been observed that it is not necessary that the most connected node is also the most central node in degree centralities.

Dyslexia on a continuum: A complex network approach.

We investigated the efficacy of graph-theoretic metrics of task-related functional brain connectivity in predicting reading difficulty and explored the hypothesis that task conditions emphasizing audiovisual integration would be especially diagnostic of reading difficulty. An fMRI study was conducted in which 24 children (8 to 14 years old) who were previously diagnosed with dyslexia completed a rhyming judgment task under three presentation modality conditions. Regression analyses found that characteristic connectivity metrics of the reading network showed a presentation modality dependent relationship with reading difficulty: Children with more segregated reading networks and those that used fewer of the available connections were those with the least severe reading difficulty. These results are consistent with the hypothesis that a lack of coordinated processing between the neural regions involved in phonological and orthographic processing contributes towards reading difficulty.

A complex network approach to environmental impact assessment

ABSTRACTThis article proposes a complex network methodology for the process of Environmental Impact Assessment (EIA) that limits subjectivity and reduces uncertainty by incorporating elements of complex systems theory in the stages of identification and assessment of the significance of environmental impacts. The proposed methodology reduces the sources of uncertainty, which emerge from the use of simplified models that analyse the environment-activity interactions in a unidirectional fashion. This proposal determines the significance of environmental impacts through multidirectional or complex causal relationships. Likewise, it limits the subjectivity of the evaluator by using these causality relationships instead of criteria based on the impacts’ attributes. The application of the proposed methodology demonstrates the advantages of (i) prioritizing the impacts according to their capacity to interact with other impacts, and (ii) the possibility to redirect the environmental management plans towards the prevention of impacts of higher complexity and to reduce the importance of derived impacts.The application of the proposed methodology reveals that the percentage of irrelevant and moderate impacts is reduced, whereas the percentage of severe and critical impacts increase, in comparison to the conventional methodologies.

Estimation of the Degree of Entropy of Russia's Foreign Economic Relations in the Choice of an International Industrial and Technological Partnership

The article studies the orderliness of Russia's foreign economic relations in the context of industrial and technological partnership with the other countries. The research is based on a combination of the logical-probabilistic approach of complex networks. It was noted that the issue of choosing the best ways for international production and technology cooperation should be resolved from the point of view of deliberate ordering in the most significant sectors for the planned outstripping development. The most significant industries for the Russian economy are identified: coal, oil and gas, machine building and high-tech sectors of the economy. The indicators of relatively low entropy (orderliness) of foreign economic relations in these significant areas are obtained. That means that there is a high potential for expanding the production and technology partnership. Although it was noted that Russia is not an active participant in industrial and technological integration with many countries in the world. The results of the study confirmed the possibility of using entropy indicators to assess the degree of development of foreign economic relations and determine the optimal directions of industrial and technological partnership.

24. Alzheimer pattern recognition in brain images using complex networks

Purpose Magnetic resonance imaging (MRI) along with complex networks is currently one of the most widely used tool for recognition of structural changes in neurodegenerative pathologies, such as Alzheimer’s disease (AD) [1] . We present a medical imaging study for AD pattern recognition performed through the combined use of complex networks and machine learning techniques. Methods A set of 300 structural T1 brain scans, from subjects of the Alzheimer’s Disease Neuroimaging Initiative, including AD patients, normal controls (NC) and mild cognitive impairment (MCI) subjects, were used. Our complex network description obtained by dividing brain scans into equal boxes can provide a convenient mathematical framework to model structural inter- and intra-subject brain similarities given in terms of Pearson’s correlation. Network features were used to feed supervised learning algorithms in order to exploit this base of knowledge for accurately discriminating the three groups. In this study, we compare four different machine learning models: Random Forests, Naive Bayes, Support Vector Machines and Neural Networks. We adopted a 5-fold cross-validation framework with a nested feature selection. Results Experimental results show that our brain connectivity model allows the extraction of complex network features able to distinguish healthy subjects respectively from illness and MCI subjects with an Area Under the Curve (AUC) greater than 0.90. Besides, we demonstrated that different supervised classification models permit a robust support to the early diagnosis of AD patients and a robust identification of brain regions significantly connected to the disease. Conclusions We can conclude the developed complex network approach has a good potential to become a method of predictive nature.

Modeling and analysis of cascading failures in projects: A complex network approach

Abstract Due to the complex integration of the tasks in a project, the failure of a single task can trigger extremely large-scale failures and destroy a considerable part of the overall project. To investigate cascading failures in projects, in this paper, the project is first abstracted as a weighted directed network composed of tasks and task interactions, after which a cascade model that takes account of the project’s self-protection mechanism is developed to examine a failure propagation process originates from a single task failure. The model is then applied to examine cascading failures in a project under three types of single task failures with varying parameters. The experiment results demonstrate that the method is very effective in predicting cascading failures, evaluating the impact of cascading failures on the project, and identifying large cascades. The insights gained from the simulation results have implications for managers in the implementation of protective measures to mitigate cascading failure risk and avert project catastrophes.

Unveiling socioeconomic differences in colombia by means of urban mobility complex networks

Social stratification lead to marked differences between people in several aspects of their lives, such as income, education, work, welfare and mobility. Here, we aim to analyze urban mobility by socioeconomic differences of travelers. In order to do so, we represent urban mobility by a complex network approach. We show that the topological properties of the networks allow to characterize mobility flows and to recognize differences in the dynamics of socioeconomic strata. We use data from origin destination surveys made for the two most populated cities in Colombia and we represent it in the form of a weighted and directed network. We found that urban mobility networks have structural differences if analyzed by socioeconomic strata of the population and unveil segregation patterns in the highest and lowest income strata.

Study of multiplicity dependence of pion fluctuations in $$\pi ^{-}$$ π - –AgBr collisions at 350 GeV using complex network approach

A complex network and chaos-based method, based on the visibility graph algorithm, is applied to study particle fluctuations in $$\pi ^{-}$$ –AgBr interactions at 350 GeV with respect to the shower multiplicity dependence. The fractal structure of the fluctuations is studied by using the power of scale freeness of visibility graph (PSVG). The selection of visibility graph as the type of complex network for our analysis is justified as this algorithm gives the most precise result with finite number of data points and this experiment has finite number of events. The topological parameters along with PSVG values are extracted and analysed. The analysis shows that the fractality character is weaker for the lowest multiplicity bin and is stronger for the highest multiplicity bin.

Trajectory Tracking on Complex Networks With Non-Identical Chaotic Nodes via Inverse Optimal Pinning Control

In this letter, a novel control approach for complex networks with non-identical nodes is developed. This approach is based on inverse optimal pinning control to track a desired trajectory. The effectiveness of the control design is illustrated via simulations on a network with nodes being different chaotic systems.

A validation strategy for in silico generated aptamers

The selection of high-affinity aptamers is of paramount interest for clinical and technological applications. A novel strategy is proposed to validate the reliability of the 3D structures of a group of anti- Angiopoietin-2 aptamers, produced in silico by using free software. In a previous literature these aptamers were processed both in vitro and in silico, by using an approach different from that here presented, and finally tested with a SPS experiment. Computational expectations and experimental outcomes did not agree.The procedure here proposed consists of three steps: a. the production of a large set of conformations for each candidate aptamer; b. the rigid docking upon the receptor; c. the topological and electrical characterization of the products. Steps a. and b. allow a global binding score of the ligand-receptor complexes based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energies. Step c. employs a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the ligand-receptor complexes. Finally, the results are discussed and compared with the literature on the same aptamers. The computational predictions are in good agreement with the known experimental measurements.

Economic Complexity and the Emergence of New Ideas

Recent research has shown a strong link between the economic complexity of a country and important economic indicators such as GDP per capita and lower inequality. However, the mechanisms underpinning this relationship remain poorly understood. While it is often argued that complex ecosystems provide a fertile ground for novel combinations of ideas leading to the emergence of impactful new technologies and industries, this process is hard to measure with aggregate data structured around industrial codes offering a lagging view of the economy. We seek to overcome this challenge by combining official data about industrial activity in UK Local Authority Districts (LADs) with a novel dataset containing the content and meta-data from almost a million UK business websites. We measure economic complexity using ECI and a Fitness based measure, which respectively capture a location’s specialization in unique, knowledge intensive sectors, and a weighted measure of economic diversity. We use a complex networks approach to topic modelling to detect ‘topics’ in the websites of businesses and analyse their relation to economic complexity and industrial sectors before going on to measure emergence by looking for novel words in the websites of businesses in different locations and sectors. We show that LADs with high ECI scores have a stronger share of companies active in emerging technologies, even after we control for their industrial composition. Further, and contrary to our initial expectations, emergent companies are more industrially diversified in locations with high ECI scores than locations with high Fitness scores. One potential reason is that the innovative sectors that high ECI locations specialize on are lead adopters of emerging technologies that are subsequently diffused into other parts of the local economy.

Folding with a protein's native shortcut network.

A complex network approach to protein folding is proposed, wherein a protein's contact map is reconceptualized as a network of shortcut edges, and folding is steered by a structural characteristic of this network. Shortcut networks are generated by a known message passing algorithm operating on protein residue networks. It is found that the shortcut networks of native structures (SCN0s) are relevant graph objects with which to study protein folding at a formal level. The logarithm form of their contact order (SCN0_lnCO) correlates significantly with folding rate of two-state and nontwo-state proteins. The clustering coefficient of SCN0s (CSCN0 ) correlates significantly with folding rate, transition-state placement and stability of two-state folders. Reasonable folding pathways for several model proteins are produced when CSCN0 is used to combine protein segments incrementally to form the native structure. The folding bias captured by CSCN0 is detectable in non-native structures, as evidenced by Molecular Dynamics simulation generated configurations for the fast folding Villin-headpiece peptide. These results support the use of shortcut networks to investigate the role protein geometry plays in the folding of both small and large globular proteins, and have implications for the design of multibody interaction schemes in folding models. One facet of this geometry is the set of native shortcut triangles, whose attributes are found to be well-suited to identify dehydrated intraprotein areas in tight turns, or at the interface of different secondary structure elements.

Data based complex network modeling and analysis of shield tunneling performance in metro construction

Shield tunneling performance depends mainly on changes in geological conditions and machine working status. Understanding its characteristics is the key to operating and controlling shield machine during the metro construction. Despite the large set of shield tunneling data in having been a big challenge in interpreting the underlying meaning, a systematical view of the shield tunneling performance has not yet been identified. In this study, a methodology for the modeling and analysis of shield tunneling performance network is proposed which aims at integrating the high dimensional data mining and the complex network approaches for shield performance evaluation. It is tested by analyzing the heterogeneous data of shield tunneling performance acquired from in the first Yangtze river crossing metro tunnel project in China. Each segment ring tunneling cycle in the construction were considered to be nodes of the network mapped while edges are determined by nodes having the similarity greater than an optimal threshold value. The construct network exhibits high clustering coefficient combined with comparatively short path lengths, which demonstrates a small world topology feature. Communities in the performance network with different size based on the complex network are detected, which provides the vital decision information for geological conditions identification and shield tunneling performance risk evaluation.

Betweenness centrality in urban networks: revealing the transportation backbone of the country from the demographic data

The theory of complex networks plays an important role in the modelling and analysis of processes in urban systems, for example, in studies of urban transport networks, the evolution of street networks, passenger flows in the city, etc. In this study we analyze a set of demographic data taken from official population census of Republic of Kazakhstan considering the complex network approach based on spatio-populational principles, namely, the estimation of the distance between cities and taking into account their populations. To determine the geopolitical importance of particular city we use very common network characteristics such as the degree of the node and the betweenness centrality. We show how the values of betweenness can reveal the main transport routes in the country and evaluate the wealth of transportation network.

Analysis of global stock index data during crisis period via complex network approach.

Considerable research has been done on the complex stock market, however, there is very little systematic work on the impact of crisis on global stock markets. For filling in these gaps, we propose a complex network method, which analyzes the effects of the 2008 global financial crisis on global main stock index from 2005 to 2010. Firstly, we construct three weighted networks. The physics-derived technique of minimum spanning tree is utilized to investigate the networks of three stages. Regional clustering is found in each network. Secondly, we construct three average threshold networks and find the small-world property in the network before and during the crisis. Finally, the dynamical change of the network community structure is deeply analyzed with different threshold. The result indicates that for large thresholds, the network before and after the crisis has a significant community structure. Though this analysis, it would be helpful to investors for making decisions regarding their portfolios or to regulators for monitoring the key nodes to ensure the overall stability of the global stock market.

A complex network approach to clinical science.

Contemporary classification systems assume that psychiatric disorders are expressions of latent disease entities. However, some critics point to the comorbidity problem and other issues that question the validity of the latent disease model. An alternative to this traditional view is the complex network approach. This approach assumes that disorders exist as systems of inter-connected elements, without requiring that the elements are expressions of latent disease entities. Depending on the structure of the network, change can occur abruptly once the network reaches a tipping point. A dynamic complex network approach could be used to develop a functional analytic case conceptualization that may predict treatment change, relapse and recovery, thereby linking nosology and treatment. In conclusion, the complex network perspective offers an alternative and less restrictive approach to the latent disease model, while offering exciting new directions for future research in psychiatry.

A novel approach to brain connectivity reveals early structural changes in Alzheimer’s disease

Objective: Recent studies have shown that complex networks along with diffusion weighted imaging (DWI) can be efficient and promising techniques for early detection of structural pathological changes in Alzheimer’s disease. Besides, connectivity studies, specifically assessing the organization of a graph and its topology, could represent the best chance to discover how brain activity is shaped and driven. Accordingly, we propose a methodology to evaluate how Alzheimer’s disease affects brain networks through a novel way to look at graph connectivity. In fact, we use the combination of network features related to brain organization with network features related to the variations in connectivity between several subjects. Approach: From a DWI brain scan we reconstruct a probabilistic tractography by evaluating the number of white matter fibers connecting two anatomical districts, thus obtaining a weighted undirected network. The nodes of this network are the cerebral regions provided by the reference brain atlas, the weights are the intensity of linkage among them. We investigated brain connectivity graphs retrieved from a set of 222 publicly available DWI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI): 47 Alzheimer’s disease (AD) patients, 52 normal controls (NC) and 123 mild cognitive impairment (MCI) subjects, this latter cohort includes 85 early and 38 late MCI subjects, EMCI and LMCI respectively. Main results: The proposed brain connectivity approach effectively characterizes Alzheimer’s disease, especially in its early stages. In fact, MCI is a prodromal phase of Alzheimer’s disease. We report a accuracy for the discrimination of NC and AD subjects and accuracies of and for the discrimination of MCI from respectively NC and AD subjects. Significance: Our complex network approach offers an innovative and effective instrument to model brain connectivity and detect in DWI tractographies early changes due to Alzheimer’s.

Change of influenza pandemics because of climate change: Complex network simulations

IntroductionAirborne influenza virus transmission is depending on climate. Infected individuals are able to travel to any country in the world within one day. In this study we combine these two insights to investigate the influence of climate change on pandemic spreading patterns of airborne infectious diseases, like influenza. Well-known recent examples for pandemics are severe acute respiratory syndrome (SARS, 2002/2003) and H1N1 (Influenza A virus subtype, 2009), which have demonstrated the vulnerability of a strongly connected world.MethodsOur study is based on a complex network approach including the following datasets: –global air traffic data (from openflights.org) with information on airports, direct flight connections, and airplane types; –global population grid [from Socioeconomic Data and Applications Center (SEDAC), NASA]; –WATCH-Forcing-Data-ERA-Interim (WFDEI) climate reanalysis data (1980–2015) and RCP6.0 climate projection data (2016–2040): temperature, specific humidity, surface air pressure, water vapour pressure.We use the dependency between water vapour pressure and influenza transmission rate to give every location around the globe a unique transmission rate time series from 1980 until 2040. Local disease development is simulated with a stochastic SEIR compartmental model. All individuals (including infectious ones) are able to migrate from location to location via air traffic to simulate global dissemination of the virus.ResultsOur results show which regions are most vulnerable to climate change in terms of influenza pandemics towards key target locations (defined by highest degree, highest population, highest betweenness centrality). Furthermore, we point out the influence of climate change on pandemics from 1980 until 2040. A significant trend in the pandemic rate of spreading can be seen on a global scale. Climate change causes an influenza pandemic to proceed 5 days slower (global average) in the year 2040 compared to the year 1980. This trend varies from country to country. For example, pandemics originating from Chad show an accelerated (6 days faster) spread.ConclusionThe presented results focus on the effect that climate change has on spreading patterns of airborne infectious diseases. The change from 1980 until 2040 of important influencing variables like population distribution, varying air traffic, vaccine research, hygiene, and healthcare are neglected to separate the impact of climate change.

Emerging topics in Internet technology: A complex networks approach

Communication networks, in general, and Internet technology, in particular, are fast‐evolving areas of research. The literature in these domains is expanding at such a fast scale that it is very difficult to understand the underlying dynamics and emerging trends. While several citation databases are gradually indexing a large set of reliable content, currently the Web of Science represents one of the most highly valued databases. In this paper, we present a complex network‐based analytical approach to analyze recent data from the Web of Science in communication networks. Taking bibliographic records from the recent period of 2014 to 2017, we model and analyze complex scientometric networks. Using bibliometric coupling applied over complex citation data we present answers to cocitation patterns of documents, cooccurrence patterns of terms, as well as the most influential articles, among others, we also present key pivot points and intellectual turning points. Complex network analysis of the data demonstrates a considerably high level of interest in two key clusters labeled descriptively as “social networks” and “computer networks.” In addition, key themes in highly cited literature were clearly identified as “communication networks,” “social networks,” and “complex networks.”

The evolution of oil trade: A complex network approach

AbstractTrade in oil has undergone significant changes in the last 20 years: technical progress has allowed the exploitation of new and previously untapped fields; the emergence of new large oil importers, such as China, has shifted the traditional patterns of demand and supply, while the desire to diversify energy sources has favored the emergence of new suppliers. This paper compares the topological properties of the network of crude oil trade over the period 1995–2014. The analysis covers both aggregate measures (such as network density and centralization) and node-specific indicators (e.g. centrality) that allow to uncover the rise (demise) of new (old) important players. Accounting for the position of each country within the network provides valuable information above and beyond traditional measures such as market shares. To investigate whether oil trade has experienced a process of globalization or, rather, regionalization, we look at the community structure of the network: the number of communities increases in the aftermath of the global financial crisis, but then goes back to its historical values. Something similar happens to the average geographic distance within each community, showing that along a regional component there are also strategic/political considerations at play. Econometric analysis suggests that high oil prices increase the likelihood that high-production-cost exporters play a more central role in the network, thus reducing the power of traditional suppliers.