Predicting multiple observations in complex systems through low-dimensional embeddings.

Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon, electroencephalogram (EEG) signals, foreign exchange market, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor, and thus has potential for applications in many other real-world systems.

Research on the crisis propagation in the global coal trade under the Russia-Ukraine conflict.

The outbreak of the 2022 Russia-Ukraine conflict exacerbated the natural gas supply shortage in European countries. European countries restarted coal-fired power plants to maintain economic and social operations. The uneven distribution of coal resources in the world makes coal international trade inevitable. The intricate trade relations between trading countries have formed a coal trade network. When a country's coal exports are limited due to geopolitical factors, it will cause coal supply risks. The risk will spread to more countries along the trade network, eventually leading to the collapse of the trade network. This paper builds a crisis propagation model of the coal supply under the Russia-Ukraine conflict using the cascading failure model. The results showed that the Czech Republic, Ireland, Portugal, and Bulgaria become abnormal as the proportion of coal exports ß increases. When the Russian Federation reduced its coal exports by 80% and countries maintained only 10% coal exports against crisis, 23 European countries were the worst. Iceland, Ireland, Turkey and other countries were spread by the indirect risk and became abnormal countries. The Czech Republic and Bulgaria were spread by multiple risk and became abnormal countries.

Adjusting the global industrial structure for minimizing global carbon emissions: A network-based multi-objective optimization approach.

Although many scholars have focused on industrial structure adjustment to find the optimal balance between carbon emission reduction and economic growth, few studies have considered the comprehensive influence of the supply chain structure on carbon emissions. Based previous studies, we proposed a novel network-based optimization model. The results showed that carbon emissions would decrease by 4.31%, 6.26 and 8.07% with GDP increasing by 5.53%. 4.45% and 2.50% in different scenarios in the proposed network-based model, which performed better than the previous non-network-based model. There were some principal sectors which played special roles in the optimization of global industrial structure. To achieve the goal of global carbon emission reduction, some sectors should significantly reduce their total outputs such as materials and energy in China, while other sectors could continue to increase their total outputs such as machinery and services in China and South Korea. The results also showed that the change rate of carbon emissions was related with the costs of carbon emissions for the GDP growth. Countries with higher costs of carbon emissions, such as China, India and Russia, would burden more responsibilities. Furthermore, we found that the changes of the industrial structures of countries (regions) were different under global carbon emission reduction. Due to the current technology limitation, the production activities of energy and material industries in developing countries, such as China and India, should be reduced. Technology exportation of developed countries in such industries would be beneficial for the global carbon reduction.

Structural decomposition analysis of global carbon emissions: The contributions of domestic and international input changes.

Finding the essential factors driving carbon emissions is vital for the carbon reduction policy-making. Different from the existing research, this paper studied the separate influence of internal and external input structural changes on global carbon emissions. We applied structural decomposition analysis (SDA) to decompose the global carbon emission change into six factors: namely, the carbon emission intensity, the domestic input structure, the international input structure, consumption pattern, consumption volume and population. The results firstly showed that the contributions of different factors to global carbon emissions changed over time. In recent five years, structural changes of domestic inputs became the principle driver of decrease in global carbon emissions. Secondly, the results showed that there were significant differences for countries in their factors for carbon emissions. In India and Russia, domestic input structural change was the major contributor to the decrease in carbon emissions. In Japan and Germany, the most important factor for the increase in carbon emissions was the international input structure. Finally, the results showed the factors for carbon emission changes were correlated to economic development. The international input structural changes significantly increased carbon emissions in high-income countries. Our findings suggested that some countries such as India and Russia, improving the usage efficiency of domestic carbon-intensive products would help reducing carbon emissions. For most high-income countries such as Japan and Germany, they should reduce the dependence on the imported carbon-intensive products by turning the external input sources to countries with technology advantages. In addition, technology exportation of high-income countries would also be beneficial for the global carbon reduction.

Identifying key sectors based on cascading effect along paths in the embodied CO2 emission flow network in Beijing-Tianjin-Hebei region, China.

The emission of carbon dioxide (CO2) is a serious environmental issue, especially in Beijing-Tianjin-Hebei region. Unlike previous studies that mainly consider the bilateral and direct connection between two sectors, this study identifies path-based key sectors by considering the cascading effect of a sector on other sectors on paths of the entire economic system. We first construct an embodied CO2 emission flow network of Beijing-Tianjin-Hebei region, combining environmental input-output analysis and complex network theory. Then, the path-based key sectors are identified by traversing the path of each sector in the network based on cascading failure theory and hypothesis extraction method. On the one hand, the results show that a small number of sectors shoulder a large proportion of the embodied CO2 emission flows from both path and sector perspectives. On the other hand, we identify some path-based key sectors that did not receive enough attention from the sector perspective. Additionally, the sum of the embodied CO2 emission flows in about 30 steps accounts for 90% of the total embodied CO2 emission flows on its supply chain path. To more effectively reduce carbon emission, sectors that connect these 30 steps should be concerned in some policy recommendations. The method proposed in this paper can complement existing methods and contribute to further reducing CO2 emissions in the Beijing-Tianjin-Hebei region.

What Induces the Energy-Water Nexus in China's Supply Chains?

Given energy and water scarcity, it is necessary to develop an in-depth understanding of the energy-water nexus in China for its sustainable development. Previous studies have focused on nexus accounting, synergy conservation, and system optimization, but its induction mechanism along the supply chains has not been uncovered. This paper proposes a top-down structural path analysis (SPA) and combines it with an environmental input-output model (EIOM) to identify the critical final demand, consumption sectors, and supply chain paths inducing the energy-water nexus. The results show that the largest final demand of water for energy production (WFE) is capital formation, while the largest final demand of energy for water supply (EFW) is urban consumption. The distribution of WFE at different production layers shows an inverted U shape. Most WFE is indirectly consumed by other sectors, such as construction, through three-step supply chain paths. In contrast, the distribution of EFW shows a L shape, and most EFW is directly consumed by the final demand. In addition, some critical supply chain paths inducing more WFE and EFW are identified. Finally, some policies targeting the energy-water nexus management are proposed, which are conducive to resource conservation and the sustainable supply of energy and water.

Information Connections among Multiple Investors: Evolutionary Local Patterns Revealed by Motifs.

The concept of motifs provides a fresh perspective for studying local patterns, which is useful for understanding the essence of a network structure. However, few previous studies have focused on the evolutionary characteristics of weighted motifs while further considering participants' differences. We study how information connections differ among multiple investors. The evolutionary 10-year trend of weighted 3-motifs in China's energy stock markets is explored for the networks of co-holding behaviors among shareholders, who are classified as companies, funds and individuals. Our works allow us to detect the preferential local patterns distributed among different agents as their fluctuate involvement in networks. We find that the diversity of shareholders contributes to the statistical significance of local patterns, while homophily always exist among individuals. Modules of information connections are stable among reserved investors, which is especially apparent among companies. Individuals prefer to keep their connections with companies and funds. Unsteady modules happen owing to strengthen links among funds during the time that they are main participants in stock markets. More details about multiple investors informationally connected in evolutionary local patterns can be detected by our work.

Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series.

In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relations between patterns as edges. We used four time series as sample data. The results of the analysis reveal some statistical evidences that the causalities between time series is in a dynamic process. It implicates that stationary long-term causalities are not suitable for some special situations. Some short-term causalities that our model recognized can be referenced to the dynamic adjustment of the decisions. The results also show that weighted degree of the nodes obeys power law distribution. This implies that a few types of causality patterns play a major role in the process of the transition and that international crude oil market is statistically significantly not random. The clustering effect appears in the transition process and different clusters have different transition characteristics which provide probability information for predicting the evolution of the causality. The approach presents a potential to analyze multivariate time series and provides important information for investors and decision makers.

Sentiment Diffusion of Public Opinions about Hot Events: Based on Complex Network.

To study the sentiment diffusion of online public opinions about hot events, we collected people's posts through web data mining techniques. We calculated the sentiment value of each post based on a sentiment dictionary. Next, we divided those posts into five different orientations of sentiments: strongly positive (P), weakly positive (p), neutral (o), weakly negative (n), and strongly negative (N). These sentiments are combined into modes through coarse graining. We constructed sentiment mode complex network of online public opinions (SMCOP) with modes as nodes and the conversion relation in chronological order between different types of modes as edges. We calculated the strength, k-plex clique, clustering coefficient and betweenness centrality of the SMCOP. The results show that the strength distribution obeys power law. Most posts' sentiments are weakly positive and neutral, whereas few are strongly negative. There are weakly positive subgroups and neutral subgroups with ppppp and ooooo as the core mode, respectively. Few modes have larger betweenness centrality values and most modes convert to each other with these higher betweenness centrality modes as mediums. Therefore, the relevant person or institutes can take measures to lead people's sentiments regarding online hot events according to the sentiment diffusion mechanism.

Words analysis of online Chinese news headlines about trending events: a complex network perspective.

Because the volume of information available online is growing at breakneck speed, keeping up with meaning and information communicated by the media and netizens is a new challenge both for scholars and for companies who must address public relations crises. Most current theories and tools are directed at identifying one website or one piece of online news and do not attempt to develop a rapid understanding of all websites and all news covering one topic. This paper represents an effort to integrate statistics, word segmentation, complex networks and visualization to analyze headlines' keywords and words relationships in online Chinese news using two samples: the 2011 Bohai Bay oil spill and the 2010 Gulf of Mexico oil spill. We gathered all the news headlines concerning the two trending events in the search results from Baidu, the most popular Chinese search engine. We used Simple Chinese Word Segmentation to segment all the headlines into words and then took words as nodes and considered adjacent relations as edges to construct word networks both using the whole sample and at the monthly level. Finally, we develop an integrated mechanism to analyze the features of words' networks based on news headlines that can account for all the keywords in the news about a particular event and therefore track the evolution of news deeply and rapidly.

Characteristics of the transmission of autoregressive sub-patterns in financial time series.

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.

Analyzing the international exergy flow network of ferrous metal ores.

This paper employs an un-weighted and weighted exergy network to study the properties of ferrous metal ores in countries worldwide and their evolution from 2002 to 2012. We find that there are few countries controlling most of the ferrous metal ore exports in terms of exergy and that the entire exergy flow network is becoming more heterogeneous though the addition of new nodes. The increasing of the average clustering coefficient indicates that the formation of an international exergy flow system and regional integration is improving. When we contrast the average out strength of exergy and the average out strength of currency, we find both similarities and differences. Prices are affected largely by human factors; thus, the growth rate of the average out strength of currency has fluctuated acutely in the eleven years from 2002 to 2012. Exergy is defined as the maximum work that can be extracted from a system and can reflect the true cost in the world, and this parameter fluctuates much less. Performing an analysis based on the two aspects of exergy and currency, we find that the network is becoming uneven.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Features of the correlation structure of price indices.

What are the features of the correlation structure of price indices? To answer this question, 5 types of price indices, including 195 specific price indices from 2003 to 2011, were selected as sample data. To build a weighted network of price indices each price index is represented by a vertex, and a positive correlation between two price indices is represented by an edge. We studied the features of the weighted network structure by applying economic theory to the analysis of complex network parameters. We found that the frequency of the price indices follows a normal distribution by counting the weighted degrees of the nodes, and we identified the price indices which have an important impact on the network's structure. We found out small groups in the weighted network by the methods of k-core and k-plex. We discovered structure holes in the network by calculating the hierarchy of the nodes. Finally, we found that the price indices weighted network has a small-world effect by calculating the shortest path. These results provide a scientific basis for macroeconomic control policies.