Coin-flipping, ball-dropping, and grass-hopping for generating random graphs from matrices of edge probabilities

Ramani, Arjun S.; Eikmeier, Nicole; Gleich, David F.

doi:10.48550/arxiv.1709.03438

Cited by 1 publication

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“…In the particular case in which the posterior distribution factors into independent distributions over each edge-as in all of the models considered here-Monte Carlo sampling of networks is trivial. One simply generates each edge independently with the appropriate probability Q ij , and there exist straightforward algorithms for doing this efficiently [17]. In cases where the edges are not independent, one can generate networks using Markov chain importance sampling [18], in which one repeatedly makes small changes A → A ′ to the network, such as the addition or removal of a single edge, then accepts those changes with the standard Metropolis-Hastings acceptance probability P a = q(A ′ )/q(A) if q(A ′ ) < q(A), 1 otherwise.…”

Section: Computation Of Network Propertiesmentioning

confidence: 99%

Network structure from rich but noisy data

2018

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Driven by growing interest in the sciences, industry, and among the broader public, a large number of empirical studies have been conducted in recent years of the structure of networks ranging from the internet and the world wide web to biological networks and social networks. The data produced by these experiments are often rich and multimodal, yet at the same time they may contain substantial measurement error. In practice, this means that the true network structure can differ greatly from naive estimates made from the raw data, and hence that conclusions drawn from those naive estimates may be significantly in error. In this paper we describe a technique that circumvents this problem and allows us to make optimal estimates of the true structure of networks in the presence of both richly textured data and significant measurement uncertainty. We give example applications to two different social networks, one derived from face-to-face interactions and one from self-reported friendships.

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Section: Computation Of Network Propertiesmentioning

confidence: 99%