利用互斥策略优化二分网络节点预测

网络节点预测研究目前主要集中在源头节点和隐藏节点预测方面，缺少新生节点预测方向的研究。以论文和关键词关系网为研究对象，利用关键词组合情况预测新论文的产生，开展新生节点预测研究。首先将论文—关键词二分网络加权投影成关键词关系网络，然后利用关键词组合在未来出现的可能性预测新论文的产生。计算这种可能性需考虑两方面影响：一种是相似性，表示关键词共同出现的倾向；一种是互斥性，描述关键词彼此排斥的倾向，如内涵高度一致的两个关键词极少同时出现。采集期刊的论文和关键词信息构建数据集，对提出的论文预测算法进行验证，并与已有算法作对比，结果显示该算法预测效果更好。

Using mutual exclusion strategy to optimize node prediction in bipartite network

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.