17 citations · 26 across the 5 of their papers we have counts for
5 papers
On Exponential Random Graph Models with Dyadic Independence
Kayvan Sadeghi
We show that the only exponential random graph model with n nodal parameters, dyads being independent, and the natural assumption of permutation-equivariant nodal parametrization i…
Towards Robust Causal Effect Identification Beyond Markov Equivalence
Kai Z. Teh, Kayvan Sadeghi, Terry Soo
Causal effect identification typically requires a fully specified causal graph, which can be difficult to obtain in practice. We provide a sufficient criterion for identifying caus…
Causal Models for Growing Networks
Gecia Bravo-Hermsdorff, Lee M. Gunderson, Kayvan Sadeghi
Real-world networks grow over time; statistical models based on node exchangeability are not appropriate. Instead of constraining the structure of the \textit{distribution} of edge…
Statistical Models for Degree Distributions of Networks
Kayvan Sadeghi, Alessandro Rinaldo
We define and study the statistical models in exponential family form whose sufficient statistics are the degree distributions and the bi-degree distributions of undirected labelle…
models for random hypergraphs with a given degree sequence
Despina Stasi, Kayvan Sadeghi, Alessandro Rinaldo +2
We introduce the beta model for random hypergraphs in order to represent the occurrence of multi-way interactions among agents in a social network. This model builds upon and gener…