Complex Network Analysis Methods Research Articles

Exploring and Visualizing the Patent Collaboration Network: A Case Study of Smart Grid Field in China

Smart grids (SGs) have been widely recognized as an enabling technology for delivering sustainable energy transitions. SGs have a positive effect on the development of the world economy and society. SG construction plays an important role in responding to global climate change and promoting the sustainable development of the world economy and society. Under such a background, this paper attempts to investigate patent collaborations of the SG field in China. Based on the application data of collaborative patents from State Intellectual Property Office (SIPO) in China, this study employs complex network theory and social network analysis (SNA) method and conducts in-depth research on the patent collaboration network of SG field in China. The trend of patent collaboration was examined, the collaboration network of SG-related patents was investigated, the network characteristics, and the network structure were also explored. The results show that the proportion of enterprises participating in patent collaboration is relatively large for SG field in China, the percentage of collaboration relationships formed by different patent applicants varies greatly, and that the State Grid Corporation of China (SGCC) plays an important role in patent collaboration in SG field currently. It can also be found that patent collaboration is an effective form of cooperative innovation among different entities. Finally, three suggestions are provided in response to the existing problems.

Establishment and Analysis of the Supernetwork Model for Nanjing Metro Transportation System

In recent years, many researchers have applied complex network theory to urban public transport network to construct complex network and analyze its network performance. The original analysis method generally uses the Space L and Space R model to establish a simple link between public sites but ignores the organic link between the overall network system and the line subsystem. As an important part of urban public transport system, subway plays an important role in alleviating traffic pressure. In this paper, a supernetwork model of Nanjing metro network is established by using the supernetwork method. Three parameters, node-hyperedge degree, hyperedge-node degree, and hyperedge degree, are proposed to describe the model. The model is compared with the traditional Space L and Space P models. The study on the supernetwork model of Nanjing metro complex network shows that the network density, network centrality, and network clustering coefficient are large, and the average network distance is small, which meets the requirements of traffic planning and design. In this study, the subway line is considered as a subsystem and further simplified as a node, so that the complex network analysis method can be applied to the new supernetwork model, expanding the thinking of complex network research.

The nanotechnology patent thicket revisited

Despite all predictions and promises, nanotechnology still seems to be more of an emerging science than a fully fledged revolution. More often than not, the patent system is blamed for this, supposedly tangling up nanotechnology in an impenetrable thicket of exclusionary rights instead of setting it free. Drawing on methods of complex network analysis, we provide empirical evidence suggesting that, at least in Europe, there is actually no patent thicket present in nanotechnology. Yet, the situation appears to be different in the USA.

Research on Geospatial Association of the Urban Agglomeration around the South China Sea Based on Marine Traffic Flow

Studying the geospatial association within the urban agglomeration around the South China Sea can provide a basis for understanding the internal development of the China-Association of Southeast Asian Nations (ASEAN) Free Trade Area (CAFTA) and provide ideas for promoting economic and trade cooperation among cities in the region. The purpose of this paper was to reflect the characteristics of the urban agglomeration association network based on big traffic data. Based on trajectory data mining and complex network analysis methods, the automatic identification system (AIS) data was used to construct the traffic flow association network of the urban agglomeration around the South China Sea and then analysis and evaluation were carried out in three aspects: Spatial distribution characteristics of marine traffic flow, analysis of spatial hierarchy and internal difference analysis of the urban agglomeration. The results show the following: (1) The distribution of marine traffic flow within the urban agglomeration around the South China Sea is characterized by polarization and localization and shows a specific power-law distribution; (2) there is a close relationship within the urban agglomeration and the core urban and the marginal urban agglomerations were apparent; (3) subgroup division of urban agglomeration around the South China Sea shows an evident geographic agglomeration phenomenon and there were significant differences between the level of economic development among subgroups; and (4) relative to static factors such as population size and economic aggregate, dynamic flow of information and capital traffic flow plays a more important role in the spatial correlation between cities. Strengthening the links among the three layers of core-intermediate-edge cities through trade and investment means enhancing cooperation among cities within the urban agglomeration and ultimately promoting sustainable regional development.

Let's get connected: A new graph theory‐based approach and toolbox for understanding braided river morphodynamics

Our understanding of braided river morphodynamics has improved significantly in recent years, however, there are still large knowledge gaps relating to both long‐term and event‐based change in braided river morphologies. Furthermore, we still lack methods that can take full advantage of the increasing availability of remotely sensed datasets that are well suited to braided river research. Network analysis based on graph theory, the mathematics of networks, offers a largely unexplored toolbox that can be applied to remotely sensed data to quantify the structure and function of braided rivers across nearly the full range of spatiotemporal scales relevant to braided river evolution. In this article, important commonalities between braided rivers and other types of complex network are described, providing a compelling argument for the wider uptake of complex network analysis methods in the study of braided rivers. We provide an overview of the extraction of graph representations of braided river networks from remotely sensed data and detail a suite of metrics for quantitative analysis of these networks. Application of these metrics as new tools for multiscale characterization of braided river planforms that improve upon traditional, spatially averaged approaches is discussed and potential approaches to network‐based analysis of braided river dynamics are proposed, drawing on a range of different concepts from braided river research and other network sciences. Finally, the potential for using graph theory metrics to validate numerical models of braided rivers is discussed.This article is categorized under: Science of Water > Methods

Spatial structural pattern and vulnerability of China-Japan-Korea shipping network

The economies of China-Japan-Korea (CJK) are complementary, with their proximity resulting in the three countries having a high degree of interdependence with respect to trade. Currently, trade among these countries relies mainly on port-centered shipping. The development of the shipping network is integral for in-depth integration of CJK trade. This paper analyzes the overall characteristics, centrality, spatial structure, and vulnerability of the CJK shipping network using the methods of complex network analysis, blocking flow theory, and interruption and deletion of hub ports. The main findings are as follows: 1) The CJK shipping network has a small average path length and clustering coefficient, and its degree distribution follows a power-law distribution, which make the network present obvious characteristics of a Barabasi-Albert scale-free. 2) The characteristics of the multi-center point of the CJK shipping network can alleviate traffic pressure. At the same time, the network shows a clear hierarchy in the port transportation system, with cargo transport relying mainly on the ‘hub port-hub port’ connection. 3) The CJK shipping network is relatively stable. Compared with ports in Japan and Korea, the main hub ports in China have a greater impact on the stability of the shipping network, in particular those ports of the central coastal region, including Shanghai, Ningbo, and Lianyungang.

Functional Connectivity Analysis of Brain Default Mode Networks Using Hamiltonian Path.

The aim of this study is to introduce Hamiltonian path to analyze functional connectivity of brain default mode networks (DMNs). Firstly, the brain DMNs in resting state are constructed with the employment of functional Magnetic Resonance Imaging (fMRI) data. Then, the Dijkstra algorithm is used to calculate the shortest path length of the node which represents each brain region, and the Hamiltonian path of the default network is solved through the improved adaptive ant colony algorithm. Finally, complex network analysis methods are introduced to discuss the node and network properties of brain functional connectivity in both normal subjects and stroke patients. The experimental result demonstrated that there are some significant differences in the properties of the DMNs between stroke patients and normal subjects, especially the length of Hamiltonian path. It also verifies the effectiveness on studying the functional connectivity of the brain DMNs by applying the proposed method of Hamiltonian path.

Hierarchical Value Chains Encompassing Freight Transportation and Logistics Sectors in the United States

This research reveals value chain (VC) clusters in which freight transportation modes (i.e., truck, rail, air, water, and pipelines), freight supporting activities (i.e., couriers and messengers and warehousing and storage), and other industry sectors have strong economic inter-dependencies. The most recent input–output data in the United States were used to construct the 2014 direct requirements matrix and generate a network of economic interdependencies between industry sectors. Community detection, a complex network analysis method, was used to reveal the Corresponding VCs in the economic network. A novel procedure uncovered hierarchical VC communities (VCCs) for each freight transportation and logistics sector. Thus, seven distinctive VCCs with unique underlying activities were found. In general, freight industries are hierarchically clustered to VCCs related to raw materials; services; metals; agriculture; textile, apparel, and paper; wood products; and others. Results are important for the areas of transportation economics, regional economic development, competitiveness, and multimodal freight transportation.

Pion Fluctuation in High-energy Collisions --- A Chaos-based Quantitative Estimation with Visibility Graph Technique

We propose a new approach for studying pion fluctuation for deeper understanding of the dynamical process involved, from a perspective of fBm-based complex network analysis method called Visibility graph Analysis. This chaos-based, rigorous, non-linear technique is applied to study the erratic behavior of multipion production in \pi^{-}-Ag/Br interactions at 350 GeV. This method can offer reliable results with finite data points. The Power of Scale-freeness of Visibility Graph denoted by-PSVG is a measure of fractality, which can be used as a quantitative parameter for the assessment of the state of a chaotic system. The event-wise fluctuation of the multipion production process can be described by this parameter-PSVG. From the analysis of the PSVG parameter, we can quantitatively confirm that fractal behavior of the particle production process depends on the target excitation and also the fractality decreases with the increase of target excitation.

Cross-Shareholdings Structural Characteristic and Evolution Analysis Based on Complex Network

This study depicts the network morphology of firms which establish ties through cross-shareholdings by the theory of complex network analysis method. It calculates some complex network properties of the cross-shareholdings network and analyzes the evolution law of network structure in nearly 7 years. The network clearly displays small world properties and scale-free properties. The cross-shareholdings network average path length and clustering coefficient is with a small amplitude fluctuation; the network structure is relatively stable. Such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the cross-shareholdings network.

Analysis of co-occurrence toponyms in web pages based on complex networks

Abstract A large number of geographical toponyms exist in web pages and other documents, providing abundant geographical resources for GIS. It is very common for toponyms to co-occur in the same documents. To investigate these relations associated with geographic entities, a novel complex network model for co-occurrence toponyms is proposed. Then, 12 toponym co-occurrence networks are constructed from the toponym sets extracted from the People’s Daily Paper documents of 2010. It is found that two toponyms have a high co-occurrence probability if they are at the same administrative level or if they possess a part-whole relationship. By applying complex network analysis methods to toponym co-occurrence networks, we find the following characteristics. (1) The navigation vertices of the co-occurrence networks can be found by degree centrality analysis. (2) The networks express strong cluster characteristics, and it takes only several steps to reach one vertex from another one, implying that the networks are small-world graphs. (3) The degree distribution satisfies the power law with an exponent of 1.7, so the networks are free-scale. (4) The networks are disassortative and have similar assortative modes, with assortative exponents of approximately 0.18 and assortative indexes less than 0. (5) The frequency of toponym co-occurrence is weakly negatively correlated with geographic distance, but more strongly negatively correlated with administrative hierarchical distance. Considering the toponym frequencies and co-occurrence relationships, a novel method based on link analysis is presented to extract the core toponyms from web pages. This method is suitable and effective for geographical information retrieval.

Researching commuting to work using the methods of complex network analysis

In the current paper the possible utilization of complex network analysis in spatial researches was investigated. The organizational and developmental regularities of networks were demonstrated from the aspect of regional development planning. The reviewed regularities provide a new approach of the regional developments. The dependencies of settlements were analysed with the application of disparity method on the basis of the commuting matrix of the census from 2011. The disparity of out-commuting exceeded the level of in-commuting in all population categories, producing a more significant dependency relation in case of out-commuting. In general, the value of disparity increases with decreasing population number in settlements and dependency grows. This can be related with decrease in the level of degree and commuting distance. According to detailed results, the method of disparity might be effectively used in additional spatial analyses as well. The community detection procedures of the complex network analysis were also applied for spatial division. Modularity optimization with the Louvain method was successfully used in the delimitation of larger territorial units. Smaller units can be created by the increase of the resolution but modularity stability deteriorates. At the same time the composition of the units changes. In the light of the results, it could be stated that regions formed by commuting relations (according to the process of regionalism) did not match the Hungarian NUTS2 statistical regions, but natural borders and NUTS-3 level administrative boundaries could be detected in more cases. The differences between the results and NUTS-3 boundaries are not unique distortions caused by the methodology but these reflect real commuting relations (the local labour system units were discussed in a previous study). The methodology might be appropriate to detect the hierarchical order of the local labour system’s units. The method is adaptable for additional analysis of spatial interactions.

A munkaerőpiaci ingázás vizsgálati lehetőségei komplex hálózatelemzéssel

A munkaerőpiaci ingázás vizsgálati lehetőségei komplex hálózatelemzéssel

Assessment on characteristics of LUCC process based on complex network in Modern Yellow River Delta, Shandong Province of China

Research on land use cover change (LUCC) has reached a mature stage and has formed a relatively complete scientific system. However, most studies analyzed the LUCC process from the view of land use types ignoring the integrality and systematicness of the land use system, which brought certain constraints to understanding the complexity and systematicness of land use process scientifically. Using the Modern Yellow River Delta (MYRD) of China as the study area, this research introduced the complex network analysis method to the study of the LUCC process by using 9 periods of land use data between 1976 and 2014 to build 8 stages of complex networks. The values of node degree, betweenness, changing proportions, and average shortest path in the networks were calculated to identify the key land use types, changing models of land use types, and the stability of land use system. Additionally, main parts of complex networks of the period from 1976 to 1995 and the period from 1995 to 2014 were selected to evaluate the changing characteristics of the LUCC process. The results indicated the area and proportion of natural wetland kept reducing, but the area and proportion of artificial wetland or non-wetland continued rising. The bare land, reed, bush, and cultivated land were the key land use types of the LUCC process. In the past 38 years, beach, bare land, reed and bush had been the output types, and the building land and salt pan had been the input types. The LUCC process has been a transfer process of natural wetland to artificial wetland and non-wetland in the past 38 years, which could be divided into land accretion process (1976 to 1995) and construction process (1995 to 2014). The land ecosystem was unstable for the period from 1990 to 2006.

Explore ideal of establishing ischemic stroke drugs group modules based on electronic medical records

Ischemic stroke is a kind of common and frequently-occurring disease in clinic. It has high morbidity and high mortality and high morbidity characteristics. It was treated may also use a variety of Chinese and western medicine in the acute phase in the large general hospital in clinical practice. According to the "modularity theory" from biology, different patients using different drugs, and with combination of drugs for the treatment of this disease may have a variety of topological structure characteristics. This study proposed in the national 15 large general hospital electronic medical records of database, the complex network constructed by acute ischemic stroke disease using drugs, adopting to the complex network analysis and data mining methods to build for different ischemic stroke patients who are many of Chinese and western medicine group modules. These modules can be explained structure and function characteristics, and to explore the appropriate crowd and evaluate drug group module efficacy and safety in use in clinic. It provide direction to optimize the clinical treatment for ischemic stroke, provide new method for the research on combination, also provide the basis for drug combination of Chinese and western solution into the guide.

Analysis of Topology and Properties on Localized Microblog Network

This paper introduces the association algorithm for the data mining project on the localized microblog network and network topology indicators. It analyses the topology and properties on Sina weibo network from three universities by using complex network theory and social network analysis method. The results show that the actual localized microblog community has obvious small world property and scale-free efiect. The relationship among microblog users is also discussed.

Factors that Promote Personal Mobility in Relation to the Social Network in Old Newtown

This study focuses on identifying factors that promote personal mobility (PM) technologies as an immediate means of responding to the mobility needs of elderly populations. It is known that in the case of new products that are largely unfamiliar to customers, the behavior and judgments of friends and acquaintances have a powerful effect on people's desire to gather information and, ultimately, to purchase. To analyze quantitatively the effect of friends and acquaintances on individuals’ decision-making behavior, the authors developed a discrete choice model considering the impact of PM product penetration. They likewise modeled friend and acquaintance relationships by applying the complex network analysis method. The empirical analysis of questionnaires collected in Koyo Newtown in Hiroshima city reveals the existence of a social conformity effect with respect to PM use preferences. The influence of friends and acquaintances in that setting has an impact equivalent to a net 5% decrease in PM rental prices.

A Novel Wireless Sensor Network Model Based on Complex Network Theory

The key issue of wireless sensor networks is to balance the energy costs of the entire network, to enhance the robustness of the entire sensor network. Sensor networks as a special kind of complex network, in particular, environmental constraints, and more from the traditional complex networks, such as Internet networks, ecological networks, social networks, is to introduce a way of wireless sensor networks way of complex networks theory and analytical method, the key lies in, which is a successful model of complex network theory and analysis methods, more suitable for the application of wireless sensor networks, in order to achieve certain characteristics of some wireless sensor networks to optimize the network. Considering multi-hop transmission of sensor network, this paper has proposed a maximum restriction on the communication radius of each sensor node; in order to improve the efficiency of energy consumption and maintain the sparsely of the entire network, this paper has also added a minimum restriction on the communication radius of each sensor node to the improved model; to balance the energy consumption of the entire network, The simulation results show that proposed improvements to the entire network more robust to random failure and energy costs are more balanced and reasonable. This is more applicable to wireless sensor networks.

Structural Inhomogeneity of Water by Complex Network Analysis

There is still an open debate regarding the structure forming capabilities of water at ambient conditions. To probe the presence of such inhomogeneities, we apply complex network analysis methods to a molecular dynamics simulation at room temperature. This study provides both a structural and quantitative characterization of kinetically homogeneous substates present in bulk water. We find that the conformation-space network is highly modular, and that structural properties of water molecules are spatially correlated over at least two solvation shells. From a kinetic point of view, the free energy surface is characterized by multiple heterogeneous metastable regions with different populations and marginal barriers separating them. The typical time scale of hopping between them is 200-400 fs. A scanning in temperature reveals that those substates can be stabilized either entropically or enthalpically. The latter resembles an icelike domain that extends for at least two solvation shells.

Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support

Traditional Chinese medicine (TCM) is a scientific discipline, which develops the related theories from the long-term clinical practices. The large-scale clinical data are the core empirical knowledge source for TCM research. This paper introduces a clinical data warehouse (CDW) system, which incorporates the structured electronic medical record (SEMR) data for medical knowledge discovery and TCM clinical decision support (CDS). We have developed the clinical reference information model (RIM) and physical data model to manage the various information entities and their relationships in TCM clinical data. An extraction-transformation-loading (ETL) tool is implemented to integrate and normalize the clinical data from different operational data sources. The CDW includes online analytical processing (OLAP) and complex network analysis (CNA) components to explore the various clinical relationships. Furthermore, the data mining and CNA methods are used to discover the valuable clinical knowledge from the data. The CDW has integrated 20,000 TCM inpatient data and 20,000 outpatient data, which contains manifestations (e.g. symptoms, physical examinations and laboratory test results), diagnoses and prescriptions as the main information components. We propose a practical solution to accomplish the large-scale clinical data integration and preprocessing tasks. Meanwhile, we have developed over 400 OLAP reports to enable the multidimensional analysis of clinical data and the case-based CDS. We have successfully conducted several interesting data mining applications. Particularly, we use various classification methods, namely support vector machine, decision tree and Bayesian network, to discover the knowledge of syndrome differentiation. Furthermore, we have applied association rule and CNA to extract the useful acupuncture point and herb combination patterns from the clinical prescriptions. A CDW system consisting of TCM clinical RIM, ETL, OLAP and data mining as the core components has been developed to facilitate the tasks of TCM knowledge discovery and CDS. We have conducted several OLAP and data mining tasks to explore the empirical knowledge from the TCM clinical data. The CDW platform would be a promising infrastructure to make full use of the TCM clinical data for scientific hypothesis generation, and promote the development of TCM from individualized empirical knowledge to large-scale evidence-based medicine.