Interactions in information spread: quantification and interpretation using stochastic block models
This addresses the issue of neglecting interactions in modeling real-world phenomena like social networks and natural language, which could lead to incorrect conclusions, though it appears incremental as it builds on existing stochastic block models.
The authors tackled the problem of modeling interactions between entities in information spread, proposing the Interactive Mixed Membership Stochastic Block Model (IMMSBM) and finding that accounting for interactions leads to up to 150% average relative changes in outcome probabilities and improves predictive power.
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.