21,504 research outputs found
Group Learning and Opinion Diffusion in a Broadcast Network
We analyze the following group learning problem in the context of opinion
diffusion: Consider a network with users, each facing options. In a
discrete time setting, at each time step, each user chooses out of the
options, and receive randomly generated rewards, whose statistics depend on the
options chosen as well as the user itself, and are unknown to the users. Each
user aims to maximize their expected total rewards over a certain time horizon
through an online learning process, i.e., a sequence of exploration (sampling
the return of each option) and exploitation (selecting empirically good
options) steps.
Within this context we consider two group learning scenarios, (1) users with
uniform preferences and (2) users with diverse preferences, and examine how a
user should construct its learning process to best extract information from
other's decisions and experiences so as to maximize its own reward. Performance
is measured in {\em weak regret}, the difference between the user's total
reward and the reward from a user-specific best single-action policy (i.e.,
always selecting the set of options generating the highest mean rewards for
this user). Within each scenario we also consider two cases: (i) when users
exchange full information, meaning they share the actual rewards they obtained
from their choices, and (ii) when users exchange limited information, e.g.,
only their choices but not rewards obtained from these choices
Dissemination of Health Information within Social Networks
In this paper, we investigate, how information about a common food born
health hazard, known as Campylobacter, spreads once it was delivered to a
random sample of individuals in France. The central question addressed here is
how individual characteristics and the various aspects of social network
influence the spread of information. A key claim of our paper is that
information diffusion processes occur in a patterned network of social ties of
heterogeneous actors. Our percolation models show that the characteristics of
the recipients of the information matter as much if not more than the
characteristics of the sender of the information in deciding whether the
information will be transmitted through a particular tie. We also found that at
least for this particular advisory, it is not the perceived need of the
recipients for the information that matters but their general interest in the
topic
Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses
Massive Open Online Courses (MOOCs) offer a new scalable paradigm for
e-learning by providing students with global exposure and opportunities for
connecting and interacting with millions of people all around the world. Very
often, students work as teams to effectively accomplish course related tasks.
However, due to lack of face to face interaction, it becomes difficult for MOOC
students to collaborate. Additionally, the instructor also faces challenges in
manually organizing students into teams because students flock to these MOOCs
in huge numbers. Thus, the proposed research is aimed at developing a robust
methodology for dynamic team formation in MOOCs, the theoretical framework for
which is grounded at the confluence of organizational team theory, social
network analysis and machine learning. A prerequisite for such an undertaking
is that we understand the fact that, each and every informal tie established
among students offers the opportunities to influence and be influenced.
Therefore, we aim to extract value from the inherent connectedness of students
in the MOOC. These connections carry with them radical implications for the way
students understand each other in the networked learning community. Our
approach will enable course instructors to automatically group students in
teams that have fairly balanced social connections with their peers, well
defined in terms of appropriately selected qualitative and quantitative network
metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of
Digital Information & Web Technologies (ICADIWT), India, February 2014 (6
pages, 3 figures
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