In recent years, large volumes of data have been massively studied by researchers and organizations. In this context, trend analysis is one of the most important areas. Typically, good prediction results are hard to obtain because of unknown variables that could explain the behaviors of the subject of the problem. This paper goes beyond standard trend identification methods that consider only historical behavior of the objects by including the structure of the information sources, i.e., social network metrics, as an additional dimension to model and predict trends over time. Results from a set of experiments indicate that including such metrics has improved the prediction accuracy. Our experiments considered the publication titles, as recorded in the Brazilian Lattes database, from all the Ph.Ds. in Computer Science registered in the Brazilian Lattes platform for the periods analyzed in order to evaluate the proposed trend prediction approach.

中文翻译：

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使用社交网络功能改进趋势分析

近年来，研究人员和组织对大量数据进行了大量研究。在这种情况下，趋势分析是最重要的领域之一。通常，由于可以解释问题主体行为的未知变量，很难获得良好的预测结果。本文超越了仅考虑对象的历史行为的标准趋势识别方法，包括信息源的结构，即社交网络度量，作为建模和预测随时间变化的趋势的附加维度。一组实验的结果表明，包含此类指标提高了预测准确性。我们的实验考虑了所有博士的出版物标题，如巴西拿铁数据库中记录的那样。