Effector Detection in Social Networks
TLDR
This paper proposes the influence-distance-based effector detection problem and provides a 3-approximation approach and proves that the optimal MLE can be obtained in polynomial time for connected directed acyclic graphs.Abstract:
In a social network, influence diffusion is the process of spreading innovations from user to user. An activation state identifies who are the active users who have adopted the target innovation. Given an activation state of a certain diffusion, effector detection aims to reveal the active users who are able to best explain the observed state. In this paper, we tackle the effector detection problem from two perspectives. The first approach is based on the influence distance that measures the chance that an active user can activate its neighbors. For a certain pair of users, the shorter the influence distance, the higher probability that one can activate the other. Given an activation state, the effectors are expected to have short influence distance to active users while long to inactive users. By this idea, we propose the influence-distance-based effector detection problem and provide a 3-approximation. Second, we address the effector detection problem by the maximum likelihood estimation (MLE) approach. We prove that the optimal MLE can be obtained in polynomial time for connected directed acyclic graphs. For general graphs, we first extract a directed acyclic subgraph that can well preserve the information in the original graph and then apply the MLE approach to the extracted subgraph to obtain the effectors. The effectiveness of our algorithms is experimentally verified via simulations on the real-world social network.read more
Citations
More filters
Journal ArticleDOI
Medical cyber-physical systems: A survey.
TL;DR: This article enriched the researches of the networked Medical Device (MD) systems to increase the efficiency and safety of the healthcare.
Journal ArticleDOI
Identification and Analysis of Driver Postures for In-Vehicle Driving Activities and Secondary Tasks Recognition
Yang Xing,Chen Lv,Zhaozhong Zhang,Huaji Wang,Xiaoxiang Na,Dongpu Cao,Efstathios Velenis,Fei-Yue Wang +7 more
TL;DR: The consumer range camera Kinect is used to monitor drivers and identify driving tasks in a real vehicle, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behavior recognition.
Journal ArticleDOI
Using swarm intelligence algorithms to detect influential individuals for influence maximization in social networks
Aybike Simsek,Resul Kara +1 more
TL;DR: A change in the structure of the IM problem is suggested in order to tailor it to swarm intelligence algorithms and to achieve a general slope on the state-space surface of its objective function, known as “reshaping”.
Journal ArticleDOI
Maximizing the Influence and Profit in Social Networks
TL;DR: By considering the role that price plays in viral marketing, a price related (PR) frame that contains PR-I and PR-L models for classic independent cascade and linear threshold models is proposed, which is a pioneer work.
Proceedings ArticleDOI
End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning
TL;DR: In this study, an end-to-end driving-related tasks recognition system is proposed, and the AIexNet is selected as the pre-trained CNN model to reduce the training cost.
References
More filters
Proceedings ArticleDOI
Rooting out the Rumor Culprit from Suspects
TL;DR: A maximum a posteriori (MAP) estimator is constructed to identify the rumor source using the susceptible-infected (SI) model and sheds insight into the behavior of the rumor spreading process not only in the asymptotic regime but also for the general finite-n regime.
Proceedings ArticleDOI
Information source detection in the SIR model: A sample path based approach
TL;DR: It is proved that for g-regular trees such that gq > 1, where g is the node degree and q is the infection probability, the estimator is within a constant distance from the actual source with high probability, independent of the number of infected nodes and the time the snapshot is taken.
Proceedings ArticleDOI
Rooting out the rumor culprit from suspects
TL;DR: In this paper, a maximum a posteriori (MAP) estimator is constructed to identify the rumor source using the susceptible-infected (SI) model. But the detection probability of the estimator depends on the number of infected nodes.
Proceedings ArticleDOI
Epidemic profiles and defense of scale-free networks
TL;DR: This paper develops methods to help design networks that preserve critical functionality and enable more effective defenses, and investigates features of networks that render them more or less defensible against worms.
Proceedings ArticleDOI
Personalized influence maximization on social networks
TL;DR: A random function is provided, with low variance guarantee, to randomly simulate the objective function of local influence maximization, and a scalable approximate algorithm is presented by exploring the local cascade structure of the target user.