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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Equivalence And Stable Isomorphism Of Groupoids, And Diagonal-Preserving Stable Isomorphisms Of Graph C*-Algebras And Leavitt Path Algebras, Toke Meier Carlsen, Efren Ruiz, Aidan Sims
Equivalence And Stable Isomorphism Of Groupoids, And Diagonal-Preserving Stable Isomorphisms Of Graph C*-Algebras And Leavitt Path Algebras, Toke Meier Carlsen, Efren Ruiz, Aidan Sims
Faculty of Engineering and Information Sciences - Papers: Part A
We prove that ample groupoids with σ-compact unit spaces are equivalent if and only if they are stably isomorphic in an appropriate sense, and relate this to Matui's notion of Kakutani equivalence. We use this result to show that diagonal-preserving stable isomorphisms of graph C*-algebras or Leavitt path algebras give rise to isomorphisms of the groupoids of the associated stabilised graphs. We deduce that the Leavitt path algebras LZ(E2) and LZ(E2-) are not stably *-isomorphic.
Sharing Social Network Data: Differentially Private Estimation Of Exponential Family Random-Graph Models, Vishesh Karwa, Pavel N. Krivitsky, Aleksandra B. Slavkovic
Sharing Social Network Data: Differentially Private Estimation Of Exponential Family Random-Graph Models, Vishesh Karwa, Pavel N. Krivitsky, Aleksandra B. Slavkovic
Faculty of Engineering and Information Sciences - Papers: Part A
Motivated by a real life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyse synthetic graphs to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case-study using a version of the Enron e-mail corpus data set demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy and supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analysing such data. …
Using Contrastive Divergence To Seed Monte Carlo Mle For Exponential-Family Random Graph Models, Pavel N. Krivitsky
Using Contrastive Divergence To Seed Monte Carlo Mle For Exponential-Family Random Graph Models, Pavel N. Krivitsky
Faculty of Engineering and Information Sciences - Papers: Part A
Exponential-family models for dependent data have applications in a wide variety of areas, but the dependence often results in an intractable likelihood, requiring either analytic approximation or MCMC-based techniques to fit, the latter requiring an initial parameter configuration to seed their simulations. A poor initial configuration can lead to slow convergence or outright failure. The approximate techniques that could be used to find them tend not to be as general as the simulation-based and require implementation separate from that of the MLE-finding algorithm. Contrastive divergence is a more recent simulation-based approximation technique that uses a series of abridged MCMC runs …