73 research outputs found
Minimizing Polarization and Disagreement in Social Networks
The rise of social media and online social networks has been a disruptive
force in society. Opinions are increasingly shaped by interactions on online
social media, and social phenomena including disagreement and polarization are
now tightly woven into everyday life. In this work we initiate the study of the
following question: given agents, each with its own initial opinion that
reflects its core value on a topic, and an opinion dynamics model, what is the
structure of a social network that minimizes {\em polarization} and {\em
disagreement} simultaneously?
This question is central to recommender systems: should a recommender system
prefer a link suggestion between two online users with similar mindsets in
order to keep disagreement low, or between two users with different opinions in
order to expose each to the other's viewpoint of the world, and decrease
overall levels of polarization? Our contributions include a mathematical
formalization of this question as an optimization problem and an exact,
time-efficient algorithm. We also prove that there always exists a network with
edges that is a approximation to the optimum.
For a fixed graph, we additionally show how to optimize our objective function
over the agents' innate opinions in polynomial time.
We perform an empirical study of our proposed methods on synthetic and
real-world data that verify their value as mining tools to better understand
the trade-off between of disagreement and polarization. We find that there is a
lot of space to reduce both polarization and disagreement in real-world
networks; for instance, on a Reddit network where users exchange comments on
politics, our methods achieve a -fold reduction in polarization
and disagreement.Comment: 19 pages (accepted, WWW 2018
A Basic Compositional Model for Spiking Neural Networks
This paper is part of a project on developing an algorithmic theory of brain
networks, based on stochastic Spiking Neural Network (SNN) models. Inspired by
tasks that seem to be solved in actual brains, we are defining abstract
problems to be solved by these networks. In our work so far, we have developed
models and algorithms for the Winner-Take-All problem from computational
neuroscience [LMP17a,Mus18], and problems of similarity detection and neural
coding [LMP17b]. We plan to consider many other problems and networks,
including both static networks and networks that learn.
This paper is about basic theory for the stochastic SNN model. In particular,
we define a simple version of the model. This version assumes that the neurons'
only state is a Boolean, indicating whether the neuron is firing or not. In
later work, we plan to develop variants of the model with more elaborate state.
We also define an external behavior notion for SNNs, which can be used for
stating requirements to be satisfied by the networks.
We then define a composition operator for SNNs. We prove that our external
behavior notion is "compositional", in the sense that the external behavior of
a composed network depends only on the external behaviors of the component
networks. We also define a hiding operator that reclassifies some output
behavior of an SNN as internal. We give basic results for hiding.
Finally, we give a formal definition of a problem to be solved by an SNN, and
give basic results showing how composition and hiding of networks affect the
problems that they solve. We illustrate our definitions with three examples:
building a circuit out of gates, building an "Attention" network out of a
"Winner-Take-All" network and a "Filter" network, and a toy example involving
combining two networks in a cyclic fashion
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