- Giorgos Georgiadis·Last edited by Giorgos Georgiadis on Jan 31, 2021Linked with GitHubContributed by
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https://arxiv.org/abs/1501.06444
- Stochastic block models for multiplex networks can give you the probability that a given node belongs to a given cluster/block over all 3 layers (agree/pass/disagree), without squishing the different relationships into one.
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Link prediction
Generic method Using participant-votes data, it is possible to predict future votes as follows: Use a tensor to store participant-votes data, one slice per positive/negative/pass votes. Use modified tensor decomposition such as CP to derive latent matrices, as follows: When updating the elements of the latent matrices, evaluate their fitness using a weight matrix. The weight elements should be 1 when the observation is present in the same place in participant-votes data 0 when the observation is missing (i.e. link to be predicted)
Jan 31, 2021Multiplex layer distance
title Pol.is Analysis / Multiplex layer distance description Multiplex layer distance https://iopscience.iop.org/article/10.1088/1367-2630/ab14b3 Here the authors suggest that if you use any type of centrality measure in each network layer (e.g. degree centrality for the positive-answers layer and 'inverse degree' centrality for the negative-answers) but convert it into 'rank'... ... you can work in a unifying way over all layers and eg define a sort of distance  between them.
Jan 31, 2021Related Literature
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Jan 31, 2021Graph generation model
title Pol.is Analysis / Graph generation model description Graph generation model We assume discrete time and that no two users arrive simultaneously at the poll, i.e. one user arrives at time $t$ and the other at time ${t^{'}}$, such that $t < {t^{'}}$. At time $t = 0$, we have $\left| {{U_0}} \right| = 0,\left| {{V_0}} \right| = n_0^v,\left| {{E_0}} \right| = 0$. This reflects the fact that the poll starts with no users, a set of initial statements and no votes. At time $t-1$, we consider the graph ${G_{t-1}} = ({U_{t-1}},{V_{t-1}},{E_{t-1}})$ as described in the previous section. At each time $t$, either a new user ${u_t} \notin {U_{t - 1}}$ participates in the poll (such that ${u_t} \cup {U_{t - 1}} = {U_t}$) or an existing user $u \in {U_{t - 1}}$ returns.
Jan 31, 2021
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