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Found 9 papers, 4 papers with code
Human mobility is well described by closed-form gravity-like models learned automatically from data
At the other end, we have complex machine learning and deep learning models, with tens of features and thousands of parameters, which predict mobility more accurately than gravity models at the cost of not being interpretable and not providing insight on human behavior.
Fundamental limits to learning closed-form mathematical models from data
We show that this model-learning problem displays a transition from a low-noise phase in which the true model can be learned, to a phase in which the observation noise is too high for the true model to be learned by any method.
Node metadata can produce predictability transitions in network inference problems
Network inference is the process of learning the properties of complex networks from data.
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies
Many studies have shown that there are regularities in the way human beings make decisions.
A Bayesian machine scientist to aid in the solution of challenging scientific problems
Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences.
Network-based models for social recommender systems
With the overwhelming online products available in recent years, there is an increasing need to filter and deliver relevant personalized advice for users.
Tensorial and bipartite block models for link prediction in layered networks and temporal networks
Because our models describe all layers simultaneously, our approach takes full advantage of the information contained in the whole network when making predictions about any particular layer.
Consistencies and inconsistencies between model selection and link prediction in networks
A principled approach to understand network structures is to formulate generative models.
Accurate and scalable social recommendation using mixed-membership stochastic block models
With ever-increasing amounts of online information available, modeling and predicting individual preferences-for books or articles, for example-is becoming more and more important.
