Expert information can be obtained by mining, extracting and integrating a large number of papers. After standardization, experts, institutions and related research networks are established by using related algorithms. When expert queries are made, the system automatically counts the relevant information of the expert in the research area, institutions and other experts cooperating with them. Finally, a visual page containing text, relational vector maps and other maps is constructed.
The left image is based on the vector graph of the relational network constructed by the query expert "Ming Li", and clicks on the picture derived from the co-author Long Jiali. Expert network vector graph counts the number of cooperation between the searched experts and other experts, and integrates the information of the organization where the experts are located. The information can be displayed by hovering the mouse over the name of the expert, and the number of cooperation can be displayed by hovering the mouse over the connection line. Double-click on the name of the expert and click on the derivative relationship network to explore the cooperative relationship between experts.
The left graph is a network graph constructed by inputting the keyword "spatio-temporal big data" and selecting three of the authors. Unlike the image of expert name construction, the network can select the experts it wants to include when initializing, and determine whether the establishment of a cooperative relationship must be a large spatial-temporal data domain. As can be seen from the figure, three experts, Chen Sijun, Peng Mingjun and Li Deren, cooperate with other experts in the field of big data in space and time. It happens that Chen Si and Peng Mingjun have cooperated with each other.