7,149 research outputs found
The Leviathan model: Absolute dominance, generalised distrust, small worlds and other patterns emerging from combining vanity with opinion propagation
We propose an opinion dynamics model that combines processes of vanity and
opinion propagation. The interactions take place between randomly chosen pairs.
During an interaction, the agents propagate their opinions about themselves and
about other people they know. Moreover, each individual is subject to vanity:
if her interlocutor seems to value her highly, then she increases her opinion
about this interlocutor. On the contrary she tends to decrease her opinion
about those who seem to undervalue her. The combination of these dynamics with
the hypothesis that the opinion propagation is more efficient when coming from
highly valued individuals, leads to different patterns when varying the
parameters. For instance, for some parameters the positive opinion links
between individuals generate a small world network. In one of the patterns,
absolute dominance of one agent alternates with a state of generalised
distrust, where all agents have a very low opinion of all the others (including
themselves). We provide some explanations of the mechanisms behind these
emergent behaviors and finally propose a discussion about their interestComment: Improved version after referees comment
Signed Networks in Social Media
Relations between users on social media sites often reflect a mixture of
positive (friendly) and negative (antagonistic) interactions. In contrast to
the bulk of research on social networks that has focused almost exclusively on
positive interpretations of links between people, we study how the interplay
between positive and negative relationships affects the structure of on-line
social networks. We connect our analyses to theories of signed networks from
social psychology. We find that the classical theory of structural balance
tends to capture certain common patterns of interaction, but that it is also at
odds with some of the fundamental phenomena we observe --- particularly related
to the evolving, directed nature of these on-line networks. We then develop an
alternate theory of status that better explains the observed edge signs and
provides insights into the underlying social mechanisms. Our work provides one
of the first large-scale evaluations of theories of signed networks using
on-line datasets, as well as providing a perspective for reasoning about social
media sites
A Model of Consistent Node Types in Signed Directed Social Networks
Signed directed social networks, in which the relationships between users can
be either positive (indicating relations such as trust) or negative (indicating
relations such as distrust), are increasingly common. Thus the interplay
between positive and negative relationships in such networks has become an
important research topic. Most recent investigations focus upon edge sign
inference using structural balance theory or social status theory. Neither of
these two theories, however, can explain an observed edge sign well when the
two nodes connected by this edge do not share a common neighbor (e.g., common
friend). In this paper we develop a novel approach to handle this situation by
applying a new model for node types. Initially, we analyze the local node
structure in a fully observed signed directed network, inferring underlying
node types. The sign of an edge between two nodes must be consistent with their
types; this explains edge signs well even when there are no common neighbors.
We show, moreover, that our approach can be extended to incorporate directed
triads, when they exist, just as in models based upon structural balance or
social status theory. We compute Bayesian node types within empirical studies
based upon partially observed Wikipedia, Slashdot, and Epinions networks in
which the largest network (Epinions) has 119K nodes and 841K edges. Our
approach yields better performance than state-of-the-art approaches for these
three signed directed networks.Comment: To appear in the IEEE/ACM International Conference on Advances in
Social Network Analysis and Mining (ASONAM), 201
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