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Multiplex layer distance

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https://iopscience.iop.org/article/10.1088/1367-2630/ab14b3

Here the authors suggest that

  1. 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'…
  2. … you can work in a unifying way over all layers and eg define a sort of distance  between them.

In the polis' case, this could mean distance from an ideal unanimous consensus (everybody agrees on all statements). This isn't interesting (or realistic) as an absolute value but could be used to find statements that can make a big difference if they are improved (ie they decrease distance from 'unanimous consensus' more than others

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Giorgos Georgiadis
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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, 2021
Stochastic block models

title Pol.is Analysis / Stochastic block models description Stochastic block models 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. > home

Jan 31, 2021
Related Literature

Citation graph Legend digraph cg { article; book [shape = rectangle]; important [penwidth = 3]; } digraph cg { horvat2012 [penwidth = 3];

Jan 31, 2021
Graph 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 ${t^{&apos;}}$, such that $t < {t^{&apos;}}$. 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 ${u_t} \notin {U_{t - 1}}$&#xA0;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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