Complex network and homonymous disambiguation-based scientific research talent cooperation community discovery method
By constructing a scientific researchers influence model covering multiple aspects, the problems of the same name ambiguity and incomplete data in the construction of scientific researchers' cooperative communities are solved, and the accuracy of cooperative relationships and the credibility of community discoveries are improved.
Patent Information
- Application Number
- CN202411777354.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-05
- Publication Date
- 2025-03-28
AI Technical Summary
The existing technology has the same name ambiguity in the construction of the cooperative community of scientific researchers, resulting in errors in the node connection in the network. Unstructured data is not considered, but only the lack of objective calculation of scientific research results data as influence scores. The lack of connected nodes during screening of network nodes, and the inability to intuitively distinguish different cooperative communities.
The discovery method of scientific research talents cooperative community based on complex networks and disambiguation of the same name is adopted. By building a scientific researcher influence model covering multiple aspects, the calculation accuracy of scientific researcher influence is improved, and the accuracy of discovering scientific researcher cooperative relationships is improved. The specific steps include obtaining the original data of scientific researchers' cooperation, building a complex network of scientific researchers' cooperation, calculating the comprehensive influence of scientific researchers, and building a community map of scientific researchers' cooperation based on these influence models.
By improving the calculation accuracy of the influence of scientific researchers, the accuracy of discovering cooperative relationships of scientific researchers is enhanced, the ambiguity problem of the same name is solved, and the data dimension of the cooperative relationship network is increased, making the results of the community discovery more credible and intuitive.