Table 1.

The explanation and calculation formulas of the indicators.

Indicator	Explanation	Calculation formula
Degree	Nodes are countries (regions) participating in international trade. Node degree ki is the number of nodes connected to node i. In directed networks, node degree is the sum of indegree and outdegree.	$K_i=\sum _{j\in N_i}{a}_{ij}$ where aij is the edge of nodes i and j. If the edge exists, aij is 1; otherwise, it is 0; Ni refers to other nodes connected to node i.
Network density (ρ)	It is the ratio of the actual number of sides (the number of trade links between countries) in the network to the maximum number of possible sides. It is used to measure the tightness of the network. The greater the density, the closer the trade network, and the more frequent the trade between countries.	$\rho =m/\left(n\left(n-1\right)\right)$ where n is the number of countries (regions) participating in trade. m is the actual number of network connections.
Weighted degree	Node weighting degree includes weighted outdegree and weighted indegree. In this paper, the weight is trade value.	$K_i^{out}\left(t\right)=\sum _j{a}_{ij}\left(t\right)w_{r_{ij}}\left(t\right)$ $K_i^{in}\left(t\right)=\sum _j{a}_{ji}\left(t\right)w_{r_{ji}}\left(t\right)$ where Kiout(t) and Kiin(t) are the weighted outdegree and weighted indegree of node i at time t; aij(aji) is the trade relationship between node i(j) and node j(i); ωrij(ωrji) is the trade value of node i(j) to node j(i).
Average clustering coefficient	The clustering coefficient indicates the degree of aggregation between a node and its adjacent nodes. The average clustering coefficient indicates the degree of clustering among nodes in the network. The clustering coefficient used in this paper reflects the aggregation degree of trade between countries, and it can be exploited to determine whether the network has the characteristics of a small world.	$C_i=\frac{E_i}{C_{K_i}^2}$ $C=\frac{1}{n}\sum _{i=1}^N{C}_i$ where ci is the clustering efficient of node i, and $C$ is the average clustering efficient.
Average geodesic distance L	It refers to the average distance between any two nodes in the network. In this paper, it means the average distance between exporting countries and importing countries, reflecting the efficiency in the process of trade circulation. The longer the average distance, the lower the efficiency. Also, the average geodesic distance is one of the criteria to determine whether the network has the characteristics of a small world.	$L=\frac{1}{n\times \left(n-1\right)}\sum _{i,j}d\left(i,j\right)$ where d(i,j) is the shortest distance from node i to j.
Betweenness centrality	It refers to the number of times a node acts as the “intermediary” of the shortest path of the other two nodes. In the trade network, the betweenness centrality reflects the control ability of a country (region) to the global trade of a commodity. The higher the centrality, the stronger the country's control ability over the commodity in global trade, and the greater the impact of the country's price or policy change on the international trade of the commodity.	$b_{mk\left(i\right)}=\frac{N_{mk\left(i\right)}}{N_{mk}}$ where Nmk is the number of paths between node m and node k; Nmk(i) is the number of paths passing through point i between node m and node k; bmk(i) is the betweenness centrality, i.e., the probability that node i is on the path between node m and node k.
Network efficiency	It is the average of the sum of the reciprocal distances between all pairs of nodes in the network. It is used to measure the efficiency of trade circulation in the network. The higher the network efficiency, the closer the global trade connection, and the more the supply and demand of international chip-related products can be met. A very low network efficiency means that the connectivity and transmission efficiency of the network is very low, which is reflected in the small number of trade relations between countries in the chip trade network, and the chip demand of most countries cannot be met. In this case, global chip security is threatened.	$E=\frac{1}{n\times \left(n-1\right)}\sum \frac{1}{d\left(i,j\right)}$ where d(i,j) is the shortest distance from node i to j.