314,670 research outputs found
Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling
Learning about the social structure of hidden and hard-to-reach populations
--- such as drug users and sex workers --- is a major goal of epidemiological
and public health research on risk behaviors and disease prevention.
Respondent-driven sampling (RDS) is a peer-referral process widely used by many
health organizations, where research subjects recruit other subjects from their
social network. In such surveys, researchers observe who recruited whom, along
with the time of recruitment and the total number of acquaintances (network
degree) of respondents. However, due to privacy concerns, the identities of
acquaintances are not disclosed. In this work, we show how to reconstruct the
underlying network structure through which the subjects are recruited. We
formulate the dynamics of RDS as a continuous-time diffusion process over the
underlying graph and derive the likelihood for the recruitment time series
under an arbitrary recruitment time distribution. We develop an efficient
stochastic optimization algorithm called RENDER (REspoNdent-Driven nEtwork
Reconstruction) that finds the network that best explains the collected data.
We support our analytical results through an exhaustive set of experiments on
both synthetic and real data.Comment: A full version with technical proofs. Accepted by AAAI-1
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Dynamic networks are used in a wide range of fields, including social network
analysis, recommender systems, and epidemiology. Representing complex networks
as structures changing over time allow network models to leverage not only
structural but also temporal patterns. However, as dynamic network literature
stems from diverse fields and makes use of inconsistent terminology, it is
challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a
lot of attention in recent years for their ability to perform well on a range
of network science tasks, such as link prediction and node classification.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. To address the challenges resulting from the fact that
this research crosses diverse fields as well as to survey dynamic graph neural
networks, this work is split into two main parts. First, to address the
ambiguity of the dynamic network terminology we establish a foundation of
dynamic networks with consistent, detailed terminology and notation. Second, we
present a comprehensive survey of dynamic graph neural network models using the
proposed terminologyComment: 28 pages, 9 figures, 8 table
Opinion Polarization by Learning from Social Feedback
We explore a new mechanism to explain polarization phenomena in opinion
dynamics in which agents evaluate alternative views on the basis of the social
feedback obtained on expressing them. High support of the favored opinion in
the social environment, is treated as a positive feedback which reinforces the
value associated to this opinion. In connected networks of sufficiently high
modularity, different groups of agents can form strong convictions of competing
opinions. Linking the social feedback process to standard equilibrium concepts
we analytically characterize sufficient conditions for the stability of
bi-polarization. While previous models have emphasized the polarization effects
of deliberative argument-based communication, our model highlights an affective
experience-based route to polarization, without assumptions about negative
influence or bounded confidence.Comment: Presented at the Social Simulation Conference (Dublin 2017
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