7 research outputs found
Majority Opinion Diffusion in Social Networks: An Adversarial Approach
We introduce and study a novel majority-based opinion diffusion model.
Consider a graph , which represents a social network. Assume that initially
a subset of nodes, called seed nodes or early adopters, are colored either
black or white, which correspond to positive or negative opinion regarding a
consumer product or a technological innovation. Then, in each round an
uncolored node, which is adjacent to at least one colored node, chooses the
most frequent color among its neighbors.
Consider a marketing campaign which advertises a product of poor quality and
its ultimate goal is that more than half of the population believe in the
quality of the product at the end of the opinion diffusion process. We focus on
three types of attackers which can select the seed nodes in a deterministic or
random fashion and manipulate almost half of them to adopt a positive opinion
toward the product (that is, to choose black color). We say that an attacker
succeeds if a majority of nodes are black at the end of the process. Our main
purpose is to characterize classes of graphs where an attacker cannot succeed.
In particular, we prove that if the maximum degree of the underlying graph is
not too large or if it has strong expansion properties, then it is fairly
resilient to such attacks.
Furthermore, we prove tight bounds on the stabilization time of the process
(that is, the number of rounds it needs to end) in both settings of choosing
the seed nodes deterministically and randomly. We also provide several hardness
results for some optimization problems regarding stabilization time and choice
of seed nodes.Comment: To appear in AAAI 202
Pairwise diffusion of preference rankings in social networks
We introduce a model of preference diffusion in which agents in a social network update their preferences based on those of their influencers in the network, and we study the dynamics of this model. Preferences are modelled as ordinal rankings over a finite set of alternatives. At each time step, some of the agents update the relative ordering of two alternatives adjacent in their current ranking with the majority view of their influencers. We consider both a synchronous and an asynchronous variant of this model. Our results show how the graphtheoretic structure of the social network and the structure of the agents' preferences affect the termination of the diffusion process and the properties of the preference profile at the time of termination
Pairwise Diffusion of Preference Rankings in Social Networks
We introduce a model of preference diffusion in which agents in a social network update their preferences based on those of their influencers in the network, and we study the dynamics of this model. Preferences are modelled as ordinal rankings over a finite set of alternatives. At each time step, some of the agents update the relative ordering of two alternatives adjacent in their current ranking with the majority view of their influencers. We consider both a synchronous and an asynchronous variant of this model. Our results show how the graphtheoretic structure of the social network and the structure of the agents' preferences affect the termination of the diffusion process and the properties of the preference profile at the time of termination