92 citations · 270 across the 13 of their papers we have counts for
15 papers
Unified framework for measuring segregation resolves how social and geographical space jointly shape connections
Johannes Happenhofer, Sahil Loomba, Till Hoffmann +2
Our understanding of how geographical and social segregation interact remains limited, as relatively few studies investigate them jointly, and existing approaches often lack a fram…
Information is localized in growing network models
Till Hoffmann, Jukka-Pekka Onnela
Mechanistic network models can capture salient characteristics of empirical networks using a small set of domain-specific, interpretable mechanisms. Yet inference remains challengi…
Latent space approaches to aggregate network data
Till Hoffmann
Large-scale network data can pose computational challenges, be expensive to acquire, and compromise the privacy of individuals in social networks. We show that the locations and sc…
Scalable Gaussian Process Inference with Stan
Till Hoffmann, Jukka-Pekka Onnela
Gaussian processes (GPs) are sophisticated distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. We…
Unifying Summary Statistic Selection for Approximate Bayesian Computation
Till Hoffmann, Jukka-Pekka Onnela
Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We characterize three different classes of summaries and d…
Thematic recommendations on knowledge graphs using multilayer networks
Mariano Beguerisse-Díaz, Dimitrios Korkinof, Till Hoffmann
We present a framework to generate and evaluate thematic recommendations based on multilayer network representations of knowledge graphs (KGs). In this representation, each layer e…
Cost-based feature selection for network model choice
Louis Raynal, Till Hoffmann, Jukka-Pekka Onnela
Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate B…
Inference of a universal social scale and segregation measures using social connectivity kernels
Till Hoffmann, Nick S. Jones
How people connect with one another is a fundamental question in the social sciences, and the resulting social networks can have a profound impact on our daily lives. Blau offered…
Community detection in networks without observing edges
Till Hoffmann, Leto Peel, Renaud Lambiotte +1
We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals…
Bayesian hierarchical modelling of weak lensing - the golden goal
Alan Heavens, Justin Alsing, Andrew Jaffe +3
To accomplish correct Bayesian inference from weak lensing shear data requires a complete statistical description of the data. The natural framework to do this is a Bayesian Hierar…
Unified treatment of the asymptotics of asymmetric kernel density estimators
Till Hoffmann, Nick S. Jones
We extend balloon and sample-smoothing estimators, two types of variable-bandwidth kernel density estimators, by a shift parameter and derive their asymptotic properties. Our appro…
Hierarchical Cosmic Shear Power Spectrum Inference
Justin Alsing, Alan Heavens, Andrew H. Jaffe +3
We develop a Bayesian hierarchical modelling approach for cosmic shear power spectrum inference, jointly sampling from the posterior distribution of the cosmic shear field and its…
Random Walks on Stochastic Temporal Networks
Till Hoffmann, Mason A. Porter, Renaud Lambiotte
In the study of dynamical processes on networks, there has been intense focus on network structure -- i.e., the arrangement of edges and their associated weights -- but the effects…
Decentralized Routing on Spatial Networks with Stochastic Edge Weights
Till Hoffmann, Renaud Lambiotte, Mason A. Porter
We investigate algorithms to find short paths in spatial networks with stochastic edge weights. Our formulation of the problem of finding short paths differs from traditional formu…
Generalized Master Equations for Non-Poisson Dynamics on Networks
Till Hoffmann, Mason A. Porter, Renaud Lambiotte
The traditional way of studying temporal networks is to aggregate the dynamics of the edges to create a static weighted network. This implicitly assumes that the edges are governed…