13 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
Social Learning with Partial Information Sharing
This work addresses the problem of sharing partial information within social
learning strategies. In traditional social learning, agents solve a distributed
multiple hypothesis testing problem by performing two operations at each
instant: first, agents incorporate information from private observations to
form their beliefs over a set of hypotheses; second, agents combine the
entirety of their beliefs locally among neighbors. Within a sufficiently
informative environment and as long as the connectivity of the network allows
information to diffuse across agents, these algorithms enable agents to learn
the true hypothesis. Instead of sharing the entirety of their beliefs, this
work considers the case in which agents will only share their beliefs regarding
one hypothesis of interest, with the purpose of evaluating its validity, and
draws conditions under which this policy does not affect truth learning. We
propose two approaches for sharing partial information, depending on whether
agents behave in a self-aware manner or not. The results show how different
learning regimes arise, depending on the approach employed and on the inherent
characteristics of the inference problem. Furthermore, the analysis
interestingly points to the possibility of deceiving the network, as long as
the evaluated hypothesis of interest is close enough to the truth
Dynamics of Information Diffusion and Social Sensing
Statistical inference using social sensors is an area that has witnessed
remarkable progress and is relevant in applications including localizing events
for targeted advertising, marketing, localization of natural disasters and
predicting sentiment of investors in financial markets. This chapter presents a
tutorial description of four important aspects of sensing-based information
diffusion in social networks from a communications/signal processing
perspective. First, diffusion models for information exchange in large scale
social networks together with social sensing via social media networks such as
Twitter is considered. Second, Bayesian social learning models and risk averse
social learning is considered with applications in finance and online
reputation systems. Third, the principle of revealed preferences arising in
micro-economics theory is used to parse datasets to determine if social sensors
are utility maximizers and then determine their utility functions. Finally, the
interaction of social sensors with YouTube channel owners is studied using time
series analysis methods. All four topics are explained in the context of actual
experimental datasets from health networks, social media and psychological
experiments. Also, algorithms are given that exploit the above models to infer
underlying events based on social sensing. The overview, insights, models and
algorithms presented in this chapter stem from recent developments in network
science, economics and signal processing. At a deeper level, this chapter
considers mean field dynamics of networks, risk averse Bayesian social learning
filtering and quickest change detection, data incest in decision making over a
directed acyclic graph of social sensors, inverse optimization problems for
utility function estimation (revealed preferences) and statistical modeling of
interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112