3 research outputs found
D2D-LSTM based Prediction of the D2D Diffusion Path in Mobile Social Networks
Recently, how to expand data transmission to reduce cell data and repeated
cell transmission has received more and more research attention. In mobile
social networks, content popularity prediction has always been an important
part of traffic offloading and expanding data dissemination. However, current
mainstream content popularity prediction methods only use the number of
downloads and shares or the distribution of user interests, which do not
consider important time and geographic location information in mobile social
networks, and all of data is from OSN which is not same as MSN. In this work,
we propose D2D Long Short-Term Memory (D2D-LSTM), a deep neural network based
on LSTM, which is designed to predict a complete D2D diffusion path. Our work
is the first attempt in the world to use real data of MSN to predict diffusion
path with deep neural networks which conforms to the D2D structure. Compared to
linear sequence networks, only learn users' social features without time
distribution or GPS distribution and files' content features, our model can
predict the propagation path more accurately (up to 85.858\%) and can reach
convergence faster (less than 100 steps) because of the neural network that
conforms to the D2D structure and combines user social features and files
features. Moreover, we can simulate generating a D2D propagation tree. After
experiment and comparison, it is found to be very similar to the ground-truth
trees. Finally, we define a user prototype refinement that can more accurately
describe the propagation sharing habits of a prototype user (including content
preferences, time preferences, and geographic location preferences), and
experimentally validate the predictions when the user prototype is added to
1000 classes, it is almost identical to the 50 categories.Comment: 9 pages, 10 fighures. arXiv admin note: text overlap with
arXiv:1705.09275 by other author
The Mass, Fake News, and Cognition Security
The wide spread of fake news in social networks is posing threats to social
stability, economic development and political democracy etc. Numerous studies
have explored the effective detection approaches of online fake news, while few
works study the intrinsic propagation and cognition mechanisms of fake news.
Since the development of cognitive science paves a promising way for the
prevention of fake news, we present a new research area called Cognition
Security (CogSec), which studies the potential impacts of fake news to human
cognition, ranging from misperception, untrusted knowledge acquisition,
targeted opinion/attitude formation, to biased decision making, and
investigates the effective ways for fake news debunking. CogSec is a
multidisciplinary research field that leverages knowledge from social science,
psychology, cognition science, neuroscience, AI and computer science. We first
propose related definitions to characterize CogSec and review the literature
history. We further investigate the key research challenges and techniques of
CogSec, including human-content cognition mechanism, social influence and
opinion diffusion, fake news detection and malicious bot detection. Finally, we
summarize the open issues and future research directions, such as early
detection of fake news, explainable fake news debunking, social contagion and
diffusion models of fake news, and so on
Neural Diffusion Model for Microscopic Cascade Prediction
The prediction of information diffusion or cascade has attracted much
attention over the last decade. Most cascade prediction works target on
predicting cascade-level macroscopic properties such as the final size of a
cascade. Existing microscopic cascade prediction models which focus on
user-level modeling either make strong assumptions on how a user gets infected
by a cascade or limit themselves to a specific scenario where "who infected
whom" information is explicitly labeled. The strong assumptions oversimplify
the complex diffusion mechanism and prevent these models from better fitting
real-world cascade data. Also, the methods which focus on specific scenarios
cannot be generalized to a general setting where the diffusion graph is
unobserved.
To overcome the drawbacks of previous works, we propose a Neural Diffusion
Model (NDM) for general microscopic cascade prediction. NDM makes relaxed
assumptions and employs deep learning techniques including attention mechanism
and convolutional network for cascade modeling. Both advantages enable our
model to go beyond the limitations of previous methods, better fit the
diffusion data and generalize to unseen cascades. Experimental results on
diffusion prediction task over four realistic cascade datasets show that our
model can achieve a relative improvement up to 26% against the best performing
baseline in terms of F1 score.Comment: 12 page