Excavating method for group research direction based on binary network diagram hierarchical clustering
A hierarchical clustering and bipartite network technology, applied in data mining, text database clustering/classification, special data processing applications, etc., can solve the complex structure of academic relationship network, without considering the relationship between members in the team and other problems
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[0083] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0084] The present invention provides a team research direction mining method based on bipartite network graph hierarchical clustering, comprising the following steps:
[0085] Step 1: Establish the author's research interest representation model G=G(V,E) based on the author's keyword bipartite network;
[0086] Where V is a set composed of author nodes and keyword nodes, that is, V={V A UV K}, where V A set V for the author A ={A 1 ,A 2 ,...,A n ,...,A N}, V K is the keyword set V K =K={k 1 ,k 2 ,...,kj ,...,k M}, N and M are the total number of authors in the team and the total number of keywords in the academic papers of all authors in the team respectively; E is the set formed by the edges between author nodes and keyword nodes, that is, E={e( A n ,k j )|A n ∈V A ,k j ∈K,w nj > 0}; if author A n The keyword lis...
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