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The refugee problem is one of the most important issues facing the international community today. It not only troubles the countries where refugees are generated but also has a great impact on the countries where refugees are influx. With the continuous development of globalization, the refugee problem is no longer a problem of a country or a region, but a global problem faced by the international community. To cope with the global refugee problem, this paper analyzes the number of refugees in 156 countries from 1990 to 2020 and transforms the refugee population data of these countries into a complex network through a time series visibility graph (VG) method. First, we categorize the income level of 156 countries and analyze the impact of income level on the increase of refugee numbers. Then, the evaluation index of the number of refugees is obtained through the VG method. Finally, a TOPSIS comprehensive evaluation method based on the entropy weight approach is employed to analyze the data. This paper includes two main contributions. First, the application of the VG method provides a new perspective for enriching the modeling of the global refugee population growth trend. Second, this paper shows that the TOPSIS evaluation method based on the entropy weight method is effective, which provides a new method for further research on the global refugee population growth trend.

As a basic industry in the country’s development, the transportation industry has a significant relationship to its normal operation for developing and constructing the national economy. The increase in carbon emissions from transport is an increasingly growing problem, and countries worldwide are also taking measures to reduce emissions. Using time series data over the period from 1990 to 2016, this paper applies the visibility graph approach to transform it into a complex network and excavate some information about the data, then evaluates all countries based on the TOPSIS method. We find that the development of transportation is an important symbol to measure the degree of modernization of a country’s transportation, and low-income countries have lower carbon emissions due to slower transportation development. The results of transportation carbon emissions are especially encouraging for the Chinese government given its long-term and sustained efforts to expand railway and waterway infrastructure, and provide a new perspective for further research on the development trend of global transportation carbon emissions. Meanwhile, it is urgent to speed up the development and use of clean energy for economically developed countries.

Important nodes can determine the internal structure of complex networks and reveal the internal relationships of real-world systems, and identifying key nodes in complex networks is one of the important research areas of complex network science. As the king of commodities, changes in the price of gold significantly impact the economic development of various countries. Especially in the early stages of the outbreak of war between Russia and Ukraine, the price of gold futures has been greatly impacted, and the systemic risks are gradually spreading. In this paper, a gold future price series is mapped into a visibility graph (VG), the characteristics of the gold price time series and key points-in-time, have been explored from the perspective of complex network. First, according to the data structure characteristics of gold futures, this paper converts the closing prices of gold futures of the New York Mercantile Exchange into a complex network through the VG model. Then, by using the complex network model to further delve into the price of gold futures, it is found that the degree distribution of the gold futures network follows a power-law distribution, and has obvious scale-free characteristics. Finally, this paper uses the visual network node shrinking algorithm and the technique for order preference by similarity to ideal solution (TOPSIS) analysis method to identify the key nodes of the gold futures visual map to find the key time nodes in the timeline of gold futures market. Analysis of the key time nodes of this market by four methods reveals that the repetition rate of the key time nodes in the methods’ top 10 ranking is as high as 82.5%, indicating that the results obtained in this paper are robust. This study introduces a new model to describe the characteristics of gold futures price series, one which can find key time nodes in gold futures prices and provide potential help for predicting gold futures prices.