2,811 research outputs found
Complex influence propagation based on trust-aware dynamic linear threshold models
Abstract To properly capture the complexity of influence propagation phenomena in real-world contexts, such as those related to viral marketing and misinformation spread, information diffusion models should fulfill a number of requirements. These include accounting for several dynamic aspects in the propagation (e.g., latency, time horizon), dealing with multiple cascades of information that might occur competitively, accounting for the contingencies that lead a user to change her/his adoption of one or alternative information items, and leveraging trust/distrust in the users' relationships and its effect of influence on the users' decisions. To the best of our knowledge, no diffusion model unifying all of the above requirements has been developed so far. In this work, we address such a challenge and propose a novel class of diffusion models, inspired by the classic linear threshold model, which are designed to deal with trust-aware, non-competitive as well as competitive time-varying propagation scenarios. Our theoretical inspection of the proposed models unveils important findings on the relations with existing linear threshold models for which properties are known about whether monotonicity and submodularity hold for the corresponding activation function. We also propose strategies for the selection of the initial spreaders of the propagation process, for both non-competitive and competitive influence propagation tasks, whose goal is to mimic contexts of misinformation spread. Our extensive experimental evaluation, which was conducted on publicly available networks and included comparison with competing methods, provides evidence on the meaningfulness and uniqueness of our models
Timeliness: A New Design Metric and a New Attack Surface
As the landscape of time-sensitive applications gains prominence in 5G/6G
communications, timeliness of information updates at network nodes has become
crucial, which is popularly quantified in the literature by the age of
information metric. However, as we devise policies to improve age of
information of our systems, we inadvertently introduce a new vulnerability for
adversaries to exploit. In this article, we comprehensively discuss the diverse
threats that age-based systems are vulnerable to. We begin with discussion on
densely interconnected networks that employ gossiping between nodes to expedite
dissemination of dynamic information in the network, and show how the age-based
nature of gossiping renders these networks uniquely susceptible to threats such
as timestomping attacks, jamming attacks, and the propagation of
misinformation. Later, we survey adversarial works within simpler network
settings, specifically in one-hop and two-hop configurations, and delve into
adversarial robustness concerning challenges posed by jamming, timestomping,
and issues related to privacy leakage. We conclude this article with future
directions that aim to address challenges posed by more intelligent adversaries
and robustness of networks to them
Countering Misinformation on Social Networks Using Graph Alterations
We restrict the propagation of misinformation in a social-media-like
environment while preserving the spread of correct information. We model the
environment as a random network of users in which each news item propagates in
the network in consecutive cascades. Existing studies suggest that the cascade
behaviors of misinformation and correct information are affected differently by
user polarization and reflexivity. We show that this difference can be used to
alter network dynamics in a way that selectively hinders the spread of
misinformation content. To implement these alterations, we introduce an
optimization-based probabilistic dropout method that randomly removes
connections between users to achieve minimal propagation of misinformation. We
use disciplined convex programming to optimize these removal probabilities over
a reduced space of possible network alterations. We test the algorithm's
effectiveness using simulated social networks. In our tests, we use both
synthetic network structures based on stochastic block models, and natural
network structures that are generated using random sampling of a dataset
collected from Twitter. The results show that on average the algorithm
decreases the cascade size of misinformation content by up to in
synthetic network tests and up to in natural network tests while
maintaining a branching ratio of at least for correct information.Comment: 10 pages, 6 figure
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