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Who Will Follow a New Topic Tomorrow?

机译：谁会跟随明天的新话题？

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摘要

When a novel research topic emerges, we are interested in discovering how the topic will propagate over the bibliography network, i.e., which author will research and publish about this topic. Inferring the underlying influence network among authors is the basis of predicting such topic adoption. Existing works infer the influence network based on past adoption cascades, which is limited by the amount and relevance of cascades collected. This work hypothesizes that the influence network structure and probabilities are the results of many factors including the social relationships and topic popularity. These heterogeneous information shall be optimized to learn the parameters that define the homogeneous influence network that can be used to predict future cascade. Experiments using DBLP data demonstrate that the proposed method outperforms the algorithm based on traditional cascade network inference in predicting novel topic adoption.
机译：当出现新的研究主题时，我们有兴趣发现该主题将如何在书目网络中传播，即作者将对此主题进行研究和发表。推断作者之间潜在的影响力网络是预测此类主题采用的基础。现有的工作基于过去的采用级联来推断影响网络，该网络受所收集级联的数量和相关性的限制。这项工作假设影响网络的结构和概率是许多因素（包括社会关系和话题受欢迎度）的结果。这些异构信息应进行优化，以学习定义可用于预测未来级联的同类影响网络的参数。使用DBLP数据进行的实验表明，该方法在预测新颖主题采用方面优于基于传统级联网络推理的算法。

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