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Network node prediction research currently focuses on the prediction of source nodes and hidden nodes, but lacks research on prediction of new nodes. This paper took the relational network of papers and keywords as the research object, used keyword combination to predict the emergence of new papers, and carried out the prediction research of new nodes . First, this essay projected and weighted the paper-keyword bipartite network into a keyword relational network, and then used the possib ility of keyword combination to predict the emergence of new papers in the future. There are two aspects to consider to calculate this possibility. One is simila rity, which indicates the tendency of keywords to co-occur ; and the other is mutual exclusion, which describes the tendency of k eywords to exclude each othe r. For example, two keywords with a high degree of concord rarely appear at the same time. Collected the papers and keywords information of the journal to con struct the dataset, it verifies the proposed new paper prediction algorithm, and compared with the existing algorithms . The results show that the node prediction algorithm proposed has better prediction effect. [ABSTRACT FROM AUTHOR]

Network node prediction research currently focuses on the prediction of source nodes and hidden nodes, but lacks research on prediction of new nodes. This paper took the relational network of papers and keywords as the research object, used keyword combination to predict the emergence of new papers, and carried out the prediction research of new nodes. First, this essay projected and weighted the paper-keyword bipartite network into a keyword relational network, and then used the possibility of keyword combination to predict the emergence of new papers in the future. There are two aspects to consider to calculate this possibility. One is similarity, which indicates the tendency of keywords to co-occur; and the other is mutual exclusion, which describes the tendency of keywords to exclude each other. For example, two keywords with a high degree of concord rarely appear at the same time. Collected the papers and keywords information of the journal to construct the dataset, it verifies the proposed new paper prediction algorithm, and compared with the existing algorithms . The results show that the node prediction algorithm proposed has better prediction effect. [ABSTRACT FROM AUTHOR]