6,618 research outputs found
Mitigating Misinformation Spreading in Social Networks Via Edge Blocking
The wide adoption of social media platforms has brought about numerous
benefits for communication and information sharing. However, it has also led to
the rapid spread of misinformation, causing significant harm to individuals,
communities, and society at large. Consequently, there has been a growing
interest in devising efficient and effective strategies to contain the spread
of misinformation. One popular countermeasure is blocking edges in the
underlying network.
We model the spread of misinformation using the classical Independent Cascade
model and study the problem of minimizing the spread by blocking a given number
of edges. We prove that this problem is computationally hard, but we propose an
intuitive community-based algorithm, which aims to detect well-connected
communities in the network and disconnect the inter-community edges. Our
experiments on various real-world social networks demonstrate that the proposed
algorithm significantly outperforms the prior methods, which mostly rely on
centrality measures
On Counteracting Byzantine Attacks in Network Coded Peer-to-Peer Networks
Random linear network coding can be used in peer-to-peer networks to increase
the efficiency of content distribution and distributed storage. However, these
systems are particularly susceptible to Byzantine attacks. We quantify the
impact of Byzantine attacks on the coded system by evaluating the probability
that a receiver node fails to correctly recover a file. We show that even for a
small probability of attack, the system fails with overwhelming probability. We
then propose a novel signature scheme that allows packet-level Byzantine
detection. This scheme allows one-hop containment of the contamination, and
saves bandwidth by allowing nodes to detect and drop the contaminated packets.
We compare the net cost of our signature scheme with various other Byzantine
schemes, and show that when the probability of Byzantine attacks is high, our
scheme is the most bandwidth efficient.Comment: 26 pages, 9 figures, Submitted to IEEE Journal on Selected Areas in
Communications (JSAC) "Mission Critical Networking
Limiting concept spread in environments with interacting concepts
The propagation of concepts in a population of agents is a form of influence spread, which can be modelled as a cascade from an initial set of individuals. In real-world environments there may be many concepts spreading and interacting. Previous work does not consider utilising concept interactions to limit the spread of a concept. In this paper we present a method for limiting concept spread, in environments where concepts interact and do not block others from spreading. We define a model that allows for the interactions between any number of concepts to be represented and, using this model, develop a solution to the influence limitation problem, which aims to minimise the spread of a target concept through the use of a secondary inhibiting concept. We present a heuristic, called maximum probable gain, and compare its performance to established heuristics for manipulating influence spread in both simulated smallworld networks and real-world networks
Blockage Prediction for Mobile UE in RIS-assisted Wireless Networks: A Deep Learning Approach
Due to significant blockage conditions in wireless networks, transmitted
signals may considerably degrade before reaching the receiver. The reliability
of the transmitted signals, therefore, may be critically problematic due to
blockages between the communicating nodes. Thanks to the ability of
Reconfigurable Intelligent Surfaces (RISs) to reflect the incident signals with
different reflection angles, this may counter the blockage effect by optimally
reflecting the transmit signals to receiving nodes, hence, improving the
wireless network's performance. With this motivation, this paper formulates a
RIS-aided wireless communication problem from a base station (BS) to a mobile
user equipment (UE). The BS is equipped with an RGB camera. We use the RGB
camera at the BS and the RIS panel to improve the system's performance while
considering signal propagating through multiple paths and the Doppler spread
for the mobile UE. First, the RGB camera is used to detect the presence of the
UE with no blockage. When unsuccessful, the RIS-assisted gain takes over and is
then used to detect if the UE is either "present but blocked" or "absent". The
problem is determined as a ternary classification problem with the goal of
maximizing the probability of UE communication blockage detection. We find the
optimal solution for the probability of predicting the blockage status for a
given RGB image and RIS-assisted data rate using a deep neural learning model.
We employ the residual network 18-layer neural network model to find this
optimal probability of blockage prediction. Extensive simulation results reveal
that our proposed RIS panel-assisted model enhances the accuracy of
maximization of the blockage prediction probability problem by over 38\%
compared to the baseline scheme
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