17,417 research outputs found
Social Learning over Weakly-Connected Graphs
In this paper, we study diffusion social learning over weakly-connected
graphs. We show that the asymmetric flow of information hinders the learning
abilities of certain agents regardless of their local observations. Under some
circumstances that we clarify in this work, a scenario of total influence (or
"mind-control") arises where a set of influential agents ends up shaping the
beliefs of non-influential agents. We derive useful closed-form expressions
that characterize this influence, and which can be used to motivate design
problems to control it. We provide simulation examples to illustrate the
results.Comment: To appear in 2017 in the IEEE Transactions on Signal and Information
Processing over Network
A Formal Model for Polarization under Confirmation Bias in Social Networks
We describe a model for polarization in multi-agent systems based on Esteban
and Ray's standard family of polarization measures from economics. Agents
evolve by updating their beliefs (opinions) based on an underlying influence
graph, as in the standard DeGroot model for social learning, but under a
confirmation bias; i.e., a discounting of opinions of agents with dissimilar
views. We show that even under this bias polarization eventually vanishes
(converges to zero) if the influence graph is strongly-connected. If the
influence graph is a regular symmetric circulation, we determine the unique
belief value to which all agents converge. Our more insightful result
establishes that, under some natural assumptions, if polarization does not
eventually vanish then either there is a disconnected subgroup of agents, or
some agent influences others more than she is influenced. We also prove that
polarization does not necessarily vanish in weakly-connected graphs under
confirmation bias. Furthermore, we show how our model relates to the classic
DeGroot model for social learning. We illustrate our model with several
simulations of a running example about polarization over vaccines and of other
case studies. The theoretical results and simulations will provide insight into
the phenomenon of polarization.Comment: arXiv admin note: substantial text overlap with arXiv:2104.11538,
arXiv:2012.0270
The Evolution of Beliefs over Signed Social Networks
We study the evolution of opinions (or beliefs) over a social network modeled
as a signed graph. The sign attached to an edge in this graph characterizes
whether the corresponding individuals or end nodes are friends (positive links)
or enemies (negative links). Pairs of nodes are randomly selected to interact
over time, and when two nodes interact, each of them updates its opinion based
on the opinion of the other node and the sign of the corresponding link. This
model generalizes DeGroot model to account for negative links: when two enemies
interact, their opinions go in opposite directions. We provide conditions for
convergence and divergence in expectation, in mean-square, and in almost sure
sense, and exhibit phase transition phenomena for these notions of convergence
depending on the parameters of the opinion update model and on the structure of
the underlying graph. We establish a {\it no-survivor} theorem, stating that
the difference in opinions of any two nodes diverges whenever opinions in the
network diverge as a whole. We also prove a {\it live-or-die} lemma, indicating
that almost surely, the opinions either converge to an agreement or diverge.
Finally, we extend our analysis to cases where opinions have hard lower and
upper limits. In these cases, we study when and how opinions may become
asymptotically clustered to the belief boundaries, and highlight the crucial
influence of (strong or weak) structural balance of the underlying network on
this clustering phenomenon
Forming Probably Stable Communities with Limited Interactions
A community needs to be partitioned into disjoint groups; each community
member has an underlying preference over the groups that they would want to be
a member of. We are interested in finding a stable community structure: one
where no subset of members wants to deviate from the current structure. We
model this setting as a hedonic game, where players are connected by an
underlying interaction network, and can only consider joining groups that are
connected subgraphs of the underlying graph. We analyze the relation between
network structure, and one's capability to infer statistically stable (also
known as PAC stable) player partitions from data. We show that when the
interaction network is a forest, one can efficiently infer PAC stable coalition
structures. Furthermore, when the underlying interaction graph is not a forest,
efficient PAC stabilizability is no longer achievable. Thus, our results
completely characterize when one can leverage the underlying graph structure in
order to compute PAC stable outcomes for hedonic games. Finally, given an
unknown underlying interaction network, we show that it is NP-hard to decide
whether there exists a forest consistent with data samples from the network.Comment: 11 pages, full version of accepted AAAI-19 pape
Learning Social Affordance Grammar from Videos: Transferring Human Interactions to Human-Robot Interactions
In this paper, we present a general framework for learning social affordance
grammar as a spatiotemporal AND-OR graph (ST-AOG) from RGB-D videos of human
interactions, and transfer the grammar to humanoids to enable a real-time
motion inference for human-robot interaction (HRI). Based on Gibbs sampling,
our weakly supervised grammar learning can automatically construct a
hierarchical representation of an interaction with long-term joint sub-tasks of
both agents and short term atomic actions of individual agents. Based on a new
RGB-D video dataset with rich instances of human interactions, our experiments
of Baxter simulation, human evaluation, and real Baxter test demonstrate that
the model learned from limited training data successfully generates human-like
behaviors in unseen scenarios and outperforms both baselines.Comment: The 2017 IEEE International Conference on Robotics and Automation
(ICRA
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