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Learning Latent Block Structure in Weighted Networks
This model learns from both the presence and weight of edges, allowing it to discover structure that would otherwise be hidden when weights are discarded or thresholded, and a Bayesian variational algorithm is described for efficiently approximating this model's posterior distribution over latent block structures.
Efficiently inferring community structure in bipartite networks
- D. Larremore, A. Clauset, Abigail Z. Jacobs
- Computer SciencePhysical review. E, Statistical, nonlinear, and…
- 12 March 2014
This work formulates a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to k-partite networks and demonstrates this model's ability to efficiently and accurately find community structure in synthetic bipartites with known structure and in real-world bipartITE networks with unknown structure.
Measurement and Fairness
It is argued that many of the harms discussed in the literature on fairness in computational systems are direct results of such mismatches, and it is shown how some of these harms could have been anticipated and mitigated if viewed through the lens of measurement modeling.
Adapting the Stochastic Block Model to Edge-Weighted Networks
This model will enable the recovery of latent structure in a broader range of network data than was previously possible and introduce a variational algorithm that efficiently approximates the model's posterior distribution for dense graphs.
A unified view of generative models for networks: models, methods, opportunities, and challenges
A unified view of generative models for networks is described that draws together many of these disparate threads and highlights the fundamental similarities and differences that span these fields.
Adapting to Non-stationarity with Growing Expert Ensembles
It is shown how to modify the ``fixed shares'' algorithm for tracking the best expert to cope with a steadily growing set of experts, obtained by fitting new models to new data as it becomes available, and obtain regret bounds for the growing ensemble.
Assembling thefacebook: Using Heterogeneity to Understand Online Social Network Assembly
It is shown that different vintages and adoption rates across this population of networks reveal temporal dynamics of the assembly process, and that assembly is only loosely related to network growth.
Detecting Friendship Within Dynamic Online Interaction Networks
This work investigates the accuracy of multiple statistical features, based either purely on temporal interaction patterns or on the cooperative nature of the interactions, for automatically extracting latent social ties within a massive online data set encompassing 18 billion interactions among 17 million individuals of the popular online game Halo: Reach.
Internet-human infrastructures: Lessons from Havana's StreetNet
This work bridge ethnographic studies and the study of social networks and organizations to understand the way that power is embedded in the structure of Havana's SNET, revealing how distributed infrastructure necessarily embeds the structural aspects of inequality distributed within that infrastructure.
The meaning and measurement of bias: lessons from natural language processing
- Abigail Z. Jacobs, Su Lin Blodgett, Solon Barocas, Hal Daumé, H. Wallach
- Computer ScienceFAT*
- 27 January 2020
This tutorial introduces the language of measurement modeling from the quantitative social sciences as a framework for examining how social, organizational, and political values enter computational systems and unpacking the varied normative concerns operationalized in different techniques for measuring "bias".