43,690 research outputs found
Improving Link Prediction in Intermittently Connected Wireless Networks by Considering Link and Proximity Stabilities
Several works have outlined the fact that the mobility in intermittently
connected wireless networks is strongly governed by human behaviors as they are
basically human-centered. It has been shown that the users' moves can be
correlated and that the social ties shared by the users highly impact their
mobility patterns and hence the network structure. Tracking these correlations
and measuring the strength of social ties have led us to propose an efficient
distributed tensor-based link prediction technique. In fact, we are convinced
that the feedback provided by such a prediction mechanism can enhance
communication protocols such as opportunistic routing protocols. In this paper,
we aim to bring out that measuring the stabilities of the link and the
proximity at two hops can improve the efficiency of the proposed link
prediction technique. To quantify these two parameters, we propose an entropy
estimator in order to measure the two stability aspects over successive time
periods. Then, we join these entropy estimations to the tensor-based link
prediction framework by designing new prediction metrics. To assess the
contribution of these entropy estimations in the enhancement of tensor-based
link prediction efficiency, we perform prediction on two real traces. Our
simulation results show that by exploiting the information corresponding to the
link stability and/or to the proximity stability, the performance of the
tensor-based link prediction technique is improved. Moreover, the results
attest that our proposal's ability to outperform other well-known prediction
metrics.Comment: Published in the proceedings of the 13th IEEE International Symposium
on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), San
Francisco, United States, 201
Latent Space Model for Multi-Modal Social Data
With the emergence of social networking services, researchers enjoy the
increasing availability of large-scale heterogenous datasets capturing online
user interactions and behaviors. Traditional analysis of techno-social systems
data has focused mainly on describing either the dynamics of social
interactions, or the attributes and behaviors of the users. However,
overwhelming empirical evidence suggests that the two dimensions affect one
another, and therefore they should be jointly modeled and analyzed in a
multi-modal framework. The benefits of such an approach include the ability to
build better predictive models, leveraging social network information as well
as user behavioral signals. To this purpose, here we propose the Constrained
Latent Space Model (CLSM), a generalized framework that combines Mixed
Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA)
incorporating a constraint that forces the latent space to concurrently
describe the multiple data modalities. We derive an efficient inference
algorithm based on Variational Expectation Maximization that has a
computational cost linear in the size of the network, thus making it feasible
to analyze massive social datasets. We validate the proposed framework on two
problems: prediction of social interactions from user attributes and behaviors,
and behavior prediction exploiting network information. We perform experiments
with a variety of multi-modal social systems, spanning location-based social
networks (Gowalla), social media services (Instagram, Orkut), e-commerce and
review sites (Amazon, Ciao), and finally citation networks (Cora). The results
indicate significant improvement in prediction accuracy over state of the art
methods, and demonstrate the flexibility of the proposed approach for
addressing a variety of different learning problems commonly occurring with
multi-modal social data.Comment: 12 pages, 7 figures, 2 table
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