2.2k citations · 9.8k across the 82 of their papers we have counts for
88 papers
Covariate Selection for Joint Latent Space Modeling of Sparse Network Data
Emma G Crenshaw, Yuhua Zhang, Jukka-Pekka Onnela
Network data are increasingly common in the social sciences and infectious disease epidemiology. Analyses often link network structure to node-level covariates, but existing method…
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…
Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
Maxwell H Wang, Till Hoffmann, Jukka-Pekka Onnela
Mechanistic models can provide an intuitive and interpretable explanation of network growth by specifying a set of generative rules. These rules can be defined by domain knowledge…
Identification and Estimation of Heterogeneous Interference Effects under Unknown Network
Yuhua Zhang, Jukka-Pekka Onnela, Shuo Sun +1
Interference--in which a unit's outcome is affected by the treatment of other units--poses significant challenges for the identification and estimation of causal effects. Most exis…
Community Detection through Recursive Partitioning in Bayesian Framework
Yuhua Zhang, Kori S. Zachrison, Renee Y. Hsia +1
Community detection involves grouping the nodes in the network and is one of the most-studied tasks in network science. Conventional methods usually require the specification of th…
Bayesian Inference for Sexual Contact Networks Using Longitudinal Survey Data
Till Hoffmann, Jukka-Pekka Onnela
Characterizing sexual contact networks is essential for understanding sexually transmitted infections, but principled parameter inference for mechanistic network models remains cha…