论文标题
信息传播中的相互作用：使用随机块模型进行量化和解释
Interactions in information spread: quantification and interpretation using stochastic block models
论文作者
论文摘要
在大多数现实世界中，很少有人能够独立于其环境发展。在社交网络中，用户的行为来自与他们互动的人，饲料中的新闻或趋势主题的人。在自然语言中，短语的含义来自单词的组合。在一般医学中，根据症状的相互作用建立了诊断。在这里，我们提出了一个新模型，即交互式混合成员随机块模型（IMMSBM），该模型研究了实体（主题标签，单词，模因等）之间相互作用的作用，并量化了它们在上述语料库中的重要性。我们发现互动在这些语料库中起着重要作用。在推理任务中，考虑到结果的非相互作用模型的平均相对变化导致了结果的平均相对变化。此外，它们的作用大大提高了模型的预测能力。我们的发现表明，在建模现实现象时忽略互动可能会导致得出不正确的结论。
In most real-world applications, it is seldom the case that a given observable evolves independently of its environment. In social networks, users' behavior results from the people they interact with, news in their feed, or trending topics. In natural language, the meaning of phrases emerges from the combination of words. In general medicine, a diagnosis is established on the basis of the interaction of symptoms. Here, we propose a new model, the Interactive Mixed Membership Stochastic Block Model (IMMSBM), which investigates the role of interactions between entities (hashtags, words, memes, etc.) and quantifies their importance within the aforementioned corpora. We find that interactions play an important role in those corpora. In inference tasks, taking them into account leads to average relative changes with respect to non-interactive models of up to 150\% in the probability of an outcome. Furthermore, their role greatly improves the predictive power of the model. Our findings suggest that neglecting interactions when modeling real-world phenomena might lead to incorrect conclusions being drawn.
