Weighted Complex Networks Research Articles

Cross-sectoral preparedness and mitigation for networked typhoon disasters with cascading effects

Urban disaster management is currently limited, because it treats typhoon disasters in isolation and does not consider the cascading effects when multiple disasters interact. In addition, the current literature does not clearly recognize cross-sector collaboration networks, which is a countermeasure for reducing the risks of urban disasters and maintaining sustainable development. This study adopted a complex network approach to examine the cascading effects of typhoon disasters and to identify the types of hazard events and vulnerability paths for urban areas. Cross-sector collaboration networks were constructed to identify the network parameters that facilitate collaborative behaviors among different sectors. Using a comparative analysis of cases (Japan and the Philippines), two weighted complex networks were constructed to simulate disaster cascading effects in urban systems. The results can be summarized as four aspects: (1) For the node degree centrality, rainstorms had the highest values in the typhoon disaster network of Japan and the Philippines. (2) Considering the node betweenness centrality, rainstorms played an extremely crucial role in typhoon disasters with cascading effects in Japan. In contrast, landslides were the most difficult disasters to mitigate in the Philippines. (3) After the analysis of node and edge removal strategy, the global efficiency of network decreased from 0.092 and 0.084 to 0.064, indicating all intentional removal strategies led to better disaster management performance than random removal strategies. (4) The results of the Exponential Random Graph Model (ERGM) indicate that network structures and node attributes were the main factors affecting the formation of cross-sector collaborative networks. These results provide information and increase understanding about the typhoon disasters with cascading effects in urban systems. This study furnishes effective disaster prevention and reduction strategies for urban managers to improve urban sustainability and resilience.

Time series classification based on complex network

Time series classification is an important topic in data mining. Time series classification methods have been studied by many researchers. A feature-weighted classification method is proposed based on complex network. There are four stages in the proposed classification algorithm. First, we obtain visible points and edge weights by applying an improved weighted visibility graph algorithm to transform the original time series into weighted complex networks. Next, weighted features are calculated based on visible points and weights, and unable-weighted features are obtained by utilizing NetworkX. After that, we combine two types of features to obtain a total features matrix. We then reconsider the weight of each independent feature by utilizing the importance function of random forest to measure the relative importance of features and construct a importance weighted features matrix. Finally, we utilize a traditional classifier called random forest to cluster the weighted features matrix and generate classification results. Simultaneously, a novel feature weight calculation method is applied during the classification process. We compare the proposed method to other classification methods and the results indicate that the proposed method can improve classification accuracy for time series datasets.

Detection and evolution analysis of TCM patent community based on weighted complex network

Detection and evolution analysis of TCM patent community based on weighted complex network

Detection and evolution analysis of TCM patent community based on weighted complex network

In order to provide help and support for the analysis, mining, research, and development of traditional Chinese medicines, and for the discovery of medicine evolution laws, a weighted complex network based medicine community detection algorithm is proposed. Firstly, a weighted network of herbs is constructed according to the correlation degrees between nodes; and then, nodes with the highest PageRank (PR) value are selected iteratively as the initial clustering centres of the community; lastly, the nodes with the highest similarity to the nodes in the community are added to the community one by one, until all the nodes are divided into the corresponding TCM community. We verify the stability and effectiveness of the algorithm on the classic test networks. The algorithm is applied to the community detection and evolution analysis of antihypertensive TCM patents to obtain the core herbs and their evolution laws.

Clusters of social impact firms: A complex network approach

With increasing interest around social impact investments, there is a pressing need to properly define the universe of social impact targets. This paper aims to identify communities of social impact firms (SIFs) ranked in terms of their compliance with the OECD criteria for impact investing. We include in the analysis the network dimension of the firms. Specifically, we assume that SIFs represent the nodes of a weighted complex network, whose weights grow when the linked nodes show similar behaviors in pursuing social impact targets. To empirically validate our model, we used a novel hand-collected dataset of SIFs across multiple countries. Our results highlight that the economic sector and country of origin do not act as a distinguishing factor among SIF communities. However, firm size seems to matter as firms which are more compliant with the social impact criteria tend to be smaller.

The robustness of the photosynthetic system I energy transfer complex network to targeted node attack and random node failure

Abstract In this article, we implement and compare 10 node removal (attack) strategies from the literature over the photosystem I (PSI) complex network of the common pea plant (Pisum sativum), representing the FRET energy transfer among its nodes/chromophores. We measure the network robustness (functioning) with four indicators. The node attack strategies and the network robustness indicators consider both the binary-topological and the weighted structure of the network. First, we find that the well-known node betweenness centrality attack, which has proven highly effective in dismantling most real-world networks’ topological connectivity, is ineffective over the PSI network. Second, the degeneracy of the node properties caused by the PSI’s higher network connectivity level induces a random-like node removal even when nodes are removed according to a specific node centrality measure. This phenomenon triggers a very low decrease of the PSI network functioning even when subjected to node attack. Such an outcome would indicate that the node attack strategies based on classic node properties, such as the degree or the betweenness centrality, may show low efficacy in dismantling real-world networks with very high connectivity levels. Last, the PSI network can be built by tuning a cut-off distance (CD) that defines the viable energy transfers among nodes/chromophores and progressively discards the lower energy transfer links among distant nodes/chromophores. This represents a ‘weight thresholding’ procedure allowing us to investigate the efficacy of the node attack strategies when links of lower weight are progressively pruned from the PSI network. We find that the best node attack strategies change by decreasing the CD, showing that the weight thresholding procedure affects the network response to node removal. This last outcome outlines the importance of investigating the stability of the system response for real-world weighted complex networks subjected to the weight thresholding procedure.

Functional Complex Networks Based on Operational Architectonics: Application on EEG-based Brain–computer Interface for Imagined Speech

A new method for analyzing brain complex dynamics and states is presented. This method constructs functional brain graphs and is comprised of two pylons: (a) Operational architectonics (OA) concept of brain and mind functioning. (b) Network neuroscience. In particular, the algorithm utilizes OA framework for a non-parametric segmentation of EEG signals, which leads to the identification of change points, namely abrupt jumps in EEG amplitude, called Rapid Transition Processes (RTPs). Subsequently, the time coordinates of RTPs are used for the generation of undirected weighted complex networks fulfilling a scale-free topology criterion, from which various network metrics of brain connectivity are estimated. These metrics form feature vectors, which can be used in machine learning algorithms for classification and/or prediction. The method is tested in classification problems on an EEG-based BCI data set, acquired from individuals during imagery pronunciation tasks of various words/vowels. The classification results, based on a Naïve Bayes classifier, show that the overall accuracies were found to be above chance level in all tested cases. This method was also compared with other state-of-the-art computational approaches commonly used for functional network generation, exhibiting competitive performance. The method can be useful to neuroscientists wishing to enhance their repository of brain research algorithms.

Transfer entropy coupled directed–weighted complex network analysis of rainfall dynamics

Transfer entropy coupled directed–weighted complex network analysis of rainfall dynamics

An abnormal motion condition monitoring method based on the dynamic model and complex network for AUV

This paper proposes a method based on the Autonomous Underwater Vehicle (AUV) dynamic model and complex network theory to effectively monitor the abnormal motion conditions of the AUV on the vertical plane. First of all, we simplify the AUV’s dynamic model and use a neural network to identify its parameters. The error between the dynamic model’s output and the AUV measurement is employed to build a weighted complex network, which describes the AUV motion condition pattern. Afterwards, the feature matrix is extracted from the complex network. Finally, the Support Vector Machine (SVM) is introduced to classify the feature matrix, and the three abnormal motion conditions are classified. The experimental results show that the method proposed in this paper can effectively monitor the abnormal motion conditions of AUV. Compared with other advanced methods, the performance of the proposed method is greatly improved.

Exploring the Characteristics of High-Speed Rail and Air Transportation Networks in China: A Weighted Network Approach

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

An electrostatics method for converting a time-series into a weighted complex network

This paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on electrostatics in physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles, on which Coulomb-like forces can be computed. This allows generating electrostatic-like graphs associated with time-series that, additionally to the existing transformations, can be also weighted and sometimes disconnected. Within this context, this paper examines the structural similarity between five different types of time-series and their associated graphs that are generated by the proposed algorithm and the visibility graph, which is currently the most popular algorithm in the literature. The analysis compares the source (original) time-series with the node-series generated by network measures (that are arranged into the node-ordering of the source time-series), in terms of a linear trend, chaotic behaviour, stationarity, periodicity, and cyclical structure. It is shown that the proposed electrostatic graph algorithm generates graphs with node-measures that are more representative of the structure of the source time-series than the visibility graph. This makes the proposed algorithm more natural rather than algebraic, in comparison with existing physics-defined methods. The overall approach also suggests a methodological framework for evaluating the structural relevance between the source time-series and their associated graphs produced by any possible transformation.

An Extended Correlation Dimension of Complex Networks.

Fractal and self-similarity are important characteristics of complex networks. The correlation dimension is one of the measures implemented to characterize the fractal nature of unweighted structures, but it has not been extended to weighted networks. In this paper, the correlation dimension is extended to the weighted networks. The proposed method uses edge-weights accumulation to obtain scale distances. It can be used not only for weighted networks but also for unweighted networks. We selected six weighted networks, including two synthetic fractal networks and four real-world networks, to validate it. The results show that the proposed method was effective for the fractal scaling analysis of weighted complex networks. Meanwhile, this method was used to analyze the fractal properties of the Newman–Watts (NW) unweighted small-world networks. Compared with other fractal dimensions, the correlation dimension is more suitable for the quantitative analysis of small-world effects.

Identifying influential nodes in weighted complex networks using an improved WVoteRank approach

Identifying influential nodes in weighted complex networks using an improved WVoteRank approach

Extracting a core structure from heterogeneous information network using h-subnet and meta-path strength

• Meta-path strength is proposed to measure the link strength of attribute edges in a heterogeneous information network (HIN). • The h-subnet extraction method for weighted complex networks is extended to extract a heterogeneous core structure from an HIN based on meta-path strength. • The extracted h-subnet contains less than 1% of the nodes and edges of the original HIN and yet can cover different features of at least one of several other core structures. Based on the analytical methodology of homogeneous networks, we present a novel method to extract a core structure from a heterogeneous network. By extending two forms of meta-paths to represent the relationships between attribute edges, we propose the meta-path strength as a measure of the link strength of attribute edges in a heterogeneous information network. Inspired by the h -subnet method for weighted complex networks, we identify important attribute edges based on the h -cutoff of meta-path strengths. Additionally, important base edges can be filtered according to the base nodes on the retained attribute edges. Therefore, a heterogeneous h -subnet can be obtained by combining important attribute edges and base edges. Two bibliographic information networks are used to evaluate the proposed method empirically, and the results indicate that the extracted heterogeneous h -subnets contain less than 1% of the nodes and edges of the original networks and can cover different features of at least one of several other core structures.

A Novel Multiobjective GDWCN-PSO Algorithm and Its Application to Medical Data Security

Nature-inspired optimization is one of the most prevalent research domains with a confounding history that fascinates the research communities. Particle Swarm Optimization is one of the well-known optimizers that belongs to the family of nature-inspired algorithms. It often suffers from premature convergence leading to a local optimum. To address this, several methods were presented using different network topologies of the particles, but either lacked accuracy or were slow. To solve these problems, an improved version of the Directed Weighted Complex Network Particle Swarm Optimization using the Genetic Algorithm (GDWCN-PSO) is presented. This method uses the concept of the Genetic Algorithm after each update to enhance convergence and diversity. Since most of the real-world applications and complex optimization problems involve more than one objective function so to suit this problem, a multiobjective version of GDWCN-PSO is also proposed and validated on standard benchmarks. To demonstrate its applicability in real-world applications, GDWCN-PSO is applied to solve the optimal key-based medical image encryption. It is one of the most challenging problems in health IoTs for protecting sensitive and confidential patient data as well as addressing the major concern of integrity and security of data in today’s advanced digital world.

An innovative approach for constructing a shipping index based on dynamic weighted complex networks

The shipping index is a barometer of the shipping market and plays an important role in reflecting the fluctuations of the shipping market. The traditional shipping index compilation method uses fixed weights for calculation, however, which cannot accurately reflect market fluctuations. Complex network theory is often used to study changes in the internal structure of complex systems, and existing studies have also proven the feasibility of analyzing shipping market networks using this method. This paper proposes approaching the shipping market as a complex system, and constructing a multi-stage shipping market network with routes as nodes and inter-route freight rate fluctuations as edges. We construct a weighted routing model based on network characteristics and propose a dynamic shipping index calculation method based on a new weight distribution. For validity verification, we use the Pearl River Shipping Index as an example. The results show that, about 80% of the periods, the original index and the New Pearl River Shipping Index generated by our calculation rise and fall in a consistent manner. Furthermore, when the market fluctuates, the newly constructed shipping index based on the complex network can better identify the changes in the shipping market: the trend changes are more obvious, and more effectively reflect the occurrences of local events in the shipping market. Finally, based on the complex network, the route weights of different periods can be constructed. A changed route weight can affect the rest of the routes through the linkage effect. The wider the scope of influence, the higher the weight of the route, and the more obvious the impact will be on the final shipping index due to freight rate fluctuations on this route.

Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

This paper proposes a novel approach to identify the key nodes and sections of the roadway network. The taxi-GPS trajectory data are regarded as mobile sensor to probe a large scale of urban traffic flows in real time. First, the urban primary roadway network model and dual roadway network model are developed, respectively, based on the weighted complex network. Second, an evaluation system of the key nodes and sections is developed from the aspects of dynamic traffic attributes and static topology. At the end, the taxi-GPS data collected in Xicheng District of Beijing, China, are analyzed. A comprehensive analysis of the spatial-temporal changes of the key nodes and sections is performed. Moreover, the repetition rate is used to evaluate the performance of the identification algorithm of key nodes and sections. The results show that the proposed method realizes the expression of topological structure and dynamic traffic attributes of the roadway network simultaneously, which is more practicable and effective in a large scale.

Forecasting rainfall using transfer entropy coupled directed–weighted complex networks

In rainfall forecasting, selection of an appropriate gauging station to forecast and identification of the appropriate explanatory variables are nontrivial tasks due to the complexity of the physical processes involved and spatiotemporal variability. In this study, for the first time, the concept of transfer entropy is coupled with the complex network analysis for rainfall forecasting by determining the nonlinear directional relationship between the stations. The proposed methodology involves determining the directional relationship between the stations in a given basin, and defining the most influenced station by using the node strength and directed clustering coefficient that are specifically developed for directed-weighted networks. Further, the obtained information flow within the basin is utilized to produce current monthly forecasts of the most influenced station. The methodology is implemented for rainfall forecasting in the Western Mediterranean Basin, Turkey, considering monthly total rainfall data from seven stations. The results indicate that the proposed methodology is useful and effective in the identification of the appropriate station to forecast and the relevant explanatory variables to serve as inputs for the artificial neural networks (ANNs) model. The results from the proposed methodology are compared with those from two widely employed input determination approaches (i.e. using rainfall from the most-correlated station in the basin and using rainfall from all the other stations). It is found that the proposed methodology significantly improves the forecasting performance of the ANN model. The results obtained in this study have broad implications for designing optimal rain gauge density, identification of the complexity of the rain gauge network structure, and interpolation (or extrapolation) of hydrological data for ungauged locations.

Vulnerability modeling and assessment in urban transit systems considering disaster chains: A weighted complex network approach

This research develops a hybrid approach that integrates disaster chains and complex networks to model and assess vulnerability in urban transit systems. A weighted complex network is established by combining various disaster chains and their frequencies in the operation of the urban transit system, where disaster events and their transmission paths in chains are considered as nodes and edges in the disaster network, respectively. Several measures are proposed to quantify the vulnerability magnitude of the disaster nodes, edges, and network from both micro-level and macro-level perspectives. A weighted complex network consisting of 31 disaster nodes and 73 edges is constructed to model the disaster transmission in the operation of the metro system. Results show that (1) The steward misoperation (Node 15), the unqualified maintenance (Node 17), and the edge E38 (from Node 17 to Node 23) are identified as the most vulnerable nodes and edge, which should be paid more attention for disaster risk reduction; (2) The network efficiency is calculated to be 0.1244, acting as an anchor for improving system resilience against disasters; (3) The proposed edge vulnerability measure proves to be more effective in discovering more vulnerable edges than the traditional edge measure. Both node and edge attack analysis are performed to discuss the robustness of the developed complex network. It is found that all the intentional attack strategies display better performance for reducing network efficiency than random attack strategies, and global attack strategies are more efficient in both node and edge attack analysis than local attack strategies. This indicates that the developed approach is necessarily effective to identify the vulnerability of disaster events and links in the urban transit system, enabling to discover the best strategy for improving the resilience of the transit system against disasters.

Time-space evolution of the goods export network of the Belt and Road countries: The view of weighted complex network

The weighted complex network is utilized to analyze the evolution of the overall structural features of the goods export network and the role transitions of each country in the network. The research suggests: 1. The network of exports of the Belt and Road countries has transformed from multi-core pattern into one extreme along with multi-core pattern; 2. China, South Korea, Russia, Singapore and Italy are the highest-ranking countries in the network. Among these countries, the influence of China is on the rise, South Korea South Korea’s influence remains basically unchanged., however, Russia, Singapore and Italy are on the decline; 3. The leading edge of Asia-Pacific block in the network has been enhanced year by year. Not only has the trade volume within the block increased to 50% of the whole network, but the trade export to other three blocks has significant increasement. The total volume of trade in European block increased greatly and its block mode has transformed from external to universal. The trade volume of the former Soviet Union block along with the West Asia-Africa block increased significantly as well, but there is still a large gap compared with the European block and Asia-Pacific block.