Interactions in information spread: quantification and interpretation using stochastic block models
arXiv:2004.04552 · doi:10.1145/3460231.3474254
Abstract
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.
17 pages, 3 figures, RecSys'21
References in corpus (6)
- Uncovering the Temporal Dynamics of Diffusion Networks
- A network inference method for large-scale unsupervised identification of novel drug-drug interactions
- Predicting human preferences using the block structure of complex social networks
- Influential spreaders for recurrent epidemics on networks
- Optimal prediction of decisions and model selection in social dilemmas using block models
- Information Interaction Profile of Choice Adoption