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Found 6 papers, 2 papers with code
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains
Conformal prediction is a widely used method to quantify the uncertainty of a classifier under the assumption of exchangeability (e. g., IID data).
Estimating Exposure to Information on Social Networks
The exact value of exposure to a piece of information is determined by two features: the structure of the underlying social network and the set of people who shared the piece of information.
Anomalous Edge Detection in Edge Exchangeable Social Network Models
This paper studies detecting anomalous edges in directed graphs that model social networks.
“What Do Your Friends Think?”: Efficient Polling Methods for Networks Using Friendship Paradoxhttps://ieeexplore.ieee.org/abstract/document/8832260
In this paper, we propose a novel neighborhood expectation polling (NEP) strategy that asks randomly sampled individuals: what is your estimate of the fraction of votes for A?
Maximum Likelihood Estimation of Power-law Degree Distributions via Friendship Paradox based Sampling
Although power-law degree distributions are ubiquitous in nature, the widely used parametric methods for estimating them (e. g. linear regression on double-logarithmic axes, maximum likelihood estimation with uniformly sampled nodes) suffer from the large variance introduced by the lack of data-points from the tail portion of the power-law degree distribution.
Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
Friendship Paradox Biases Perceptions in Directed Networks
For example, in an online network of a social media platform, the number of people who mention a topic in their posts---i. e., its global popularity---can be dramatically different from how people see it in their social feeds---i. e., its perceived popularity---where the feeds aggregate their friends' posts.
Social and Information Networks Physics and Society
