27,916 research outputs found
Role of social environment and social clustering in spread of opinions in co-evolving networks
Taking a pragmatic approach to the processes involved in the phenomena of
collective opinion formation, we investigate two specific modifications to the
co-evolving network voter model of opinion formation, studied by Holme and
Newman [1]. First, we replace the rewiring probability parameter by a
distribution of probability of accepting or rejecting opinions between
individuals, accounting for the asymmetric influences in relationships among
individuals in a social group. Second, we modify the rewiring step by a
path-length-based preference for rewiring that reinforces local clustering. We
have investigated the influences of these modifications on the outcomes of the
simulations of this model. We found that varying the shape of the distribution
of probability of accepting or rejecting opinions can lead to the emergence of
two qualitatively distinct final states, one having several isolated connected
components each in internal consensus leading to the existence of diverse set
of opinions and the other having one single dominant connected component with
each node within it having the same opinion. Furthermore, and more importantly,
we found that the initial clustering in network can also induce similar
transitions. Our investigation also brings forward that these transitions are
governed by a weak and complex dependence on system size. We found that the
networks in the final states of the model have rich structural properties
including the small world property for some parameter regimes. [1] P. Holme and
M. Newman, Phys. Rev. E 74, 056108 (2006)
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
Dynamics of deceptive interactions in social networks
In this paper we examine the role of lies in human social relations by
implementing some salient characteristics of deceptive interactions into an
opinion formation model, so as to describe the dynamical behaviour of a social
network more realistically. In this model we take into account such basic
properties of social networks as the dynamics of the intensity of interactions,
the influence of public opinion, and the fact that in every human interaction
it might be convenient to deceive or withhold information depending on the
instantaneous situation of each individual in the network. We find that lies
shape the topology of social networks, especially the formation of tightly
linked, small communities with loose connections between them. We also find
that agents with a larger proportion of deceptive interactions are the ones
that connect communities of different opinion, and in this sense they have
substantial centrality in the network. We then discuss the consequences of
these results for the social behaviour of humans and predict the changes that
could arise due to a varying tolerance for lies in society.Comment: 17 pages, 8 figures; Supplementary Information (3 pages, 1 figure
The Impact of Network Flows on Community Formation in Models of Opinion Dynamics
We study dynamics of opinion formation in a network of coupled agents. As the
network evolves to a steady state, opinions of agents within the same community
converge faster than those of other agents. This framework allows us to study
how network topology and network flow, which mediates the transfer of opinions
between agents, both affect the formation of communities. In traditional models
of opinion dynamics, agents are coupled via conservative flows, which result in
one-to-one opinion transfer. However, social interactions are often
non-conservative, resulting in one-to-many transfer of opinions. We study
opinion formation in networks using one-to-one and one-to-many interactions and
show that they lead to different community structure within the same network.Comment: accepted for publication in The Journal of Mathematical Sociology.
arXiv admin note: text overlap with arXiv:1201.238
Complex networks analysis in socioeconomic models
This chapter aims at reviewing complex networks models and methods that were
either developed for or applied to socioeconomic issues, and pertinent to the
theme of New Economic Geography. After an introduction to the foundations of
the field of complex networks, the present summary adds insights on the
statistical mechanical approach, and on the most relevant computational aspects
for the treatment of these systems. As the most frequently used model for
interacting agent-based systems, a brief description of the statistical
mechanics of the classical Ising model on regular lattices, together with
recent extensions of the same model on small-world Watts-Strogatz and
scale-free Albert-Barabasi complex networks is included. Other sections of the
chapter are devoted to applications of complex networks to economics, finance,
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issues, including results for opinion and citation networks.
Finally, some avenues for future research are introduced before summarizing the
main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared
for Complexity and Geographical Economics - Topics and Tools, P.
Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published
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