432 research outputs found
Fake News Detection in Social Networks via Crowd Signals
Our work considers leveraging crowd signals for detecting fake news and is
motivated by tools recently introduced by Facebook that enable users to flag
fake news. By aggregating users' flags, our goal is to select a small subset of
news every day, send them to an expert (e.g., via a third-party fact-checking
organization), and stop the spread of news identified as fake by an expert. The
main objective of our work is to minimize the spread of misinformation by
stopping the propagation of fake news in the network. It is especially
challenging to achieve this objective as it requires detecting fake news with
high-confidence as quickly as possible. We show that in order to leverage
users' flags efficiently, it is crucial to learn about users' flagging
accuracy. We develop a novel algorithm, DETECTIVE, that performs Bayesian
inference for detecting fake news and jointly learns about users' flagging
accuracy over time. Our algorithm employs posterior sampling to actively trade
off exploitation (selecting news that maximize the objective value at a given
epoch) and exploration (selecting news that maximize the value of information
towards learning about users' flagging accuracy). We demonstrate the
effectiveness of our approach via extensive experiments and show the power of
leveraging community signals for fake news detection
Formal analysis techniques for gossiping protocols
We give a survey of formal verification techniques that can be used to corroborate existing experimental results for gossiping protocols in a rigorous manner. We present properties of interest for gossiping protocols and discuss how various formal evaluation techniques can be employed to predict them
Revisiting Interval Graphs for Network Science
The vertices of an interval graph represent intervals over a real line where
overlapping intervals denote that their corresponding vertices are adjacent.
This implies that the vertices are measurable by a metric and there exists a
linear structure in the system. The generalization is an embedding of a graph
onto a multi-dimensional Euclidean space and it was used by scientists to study
the multi-relational complexity of ecology. However the research went out of
fashion in the 1980s and was not revisited when Network Science recently
expressed interests with multi-relational networks known as multiplexes. This
paper studies interval graphs from the perspective of Network Science
A Comparative Study of Wireless Sensor Networks and Their Routing Protocols
Recent developments in the area of micro-sensor devices have accelerated advances in the sensor networks field leading to many new protocols specifically designed for wireless sensor networks (WSNs). Wireless sensor networks with hundreds to thousands of sensor nodes can gather information from an unattended location and transmit the gathered data to a particular user, depending on the application. These sensor nodes have some constraints due to their limited energy, storage capacity and computing power. Data are routed from one node to other using different routing protocols. There are a number of routing protocols for wireless sensor networks. In this review article, we discuss the architecture of wireless sensor networks. Further, we categorize the routing protocols according to some key factors and summarize their mode of operation. Finally, we provide a comparative study on these various protocols
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