scholarly journals ITDPM: An Internet Topology Dynamic Propagation Model Based on Generative Adversarial Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hangyu Hu ◽  
Xuemeng Zhai ◽  
Gaolei Fei ◽  
Guangmin Hu
Keyword(s):  
Network Control ◽  
Propagation Model ◽  
Generative Adversarial Networks ◽  
Network Data ◽  
Information Propagation ◽  
Adversarial Learning ◽  
Network Information ◽  
Internet Topology ◽  
Dynamic Propagation ◽  
Influential Nodes

Network information propagation analysis is gaining a more important role in network vulnerability analysis domain for preventing potential risks and threats. Identifying the influential source nodes is one of the most important problems to analyze information propagation. Traditional methods mainly focus on extracting nodes that have high degrees or local clustering coefficients. However, these nodes are not necessarily the high influential nodes in many real-world complex networks. Therefore, we propose a novel method for detecting high influential nodes based on Internet Topology Dynamic Propagation Model (ITDPM). The model consists of two processing stages: the generator and the discriminator like the generative adversarial networks (GANs). The generator stage generates the optimal source-driven nodes based on the improved network control theory and node importance characteristics, while the discriminator stage trains the information propagation process and feeds back the outputs to the generator for performing iterative optimization. Based on the generative adversarial learning, the optimal source-driven nodes are then updated in each step via network information dynamic propagation. We apply our method to random-generated complex network data and real network data; the experimental results show that our model has notable performance on identifying the most influential nodes during network operation.

Information propagation based on historical memory

2019 ◽  
Vol 30 (12) ◽  
pp. 2050005 ◽  
Author(s):  
Fuzhong Nian ◽  
Anhui Cong ◽  
Rendong Liu
Keyword(s):  
Network Model ◽  
Critical Path ◽  
Historical Memory ◽  
Propagation Model ◽  
Information Propagation ◽  
Core Network ◽  
Memory Strength ◽  
Network Information ◽  
Long Time ◽  
With Memory

This paper aims at the phenomenon of information selective propagation based on historical memory. A network model with memory strength and edge strength is established. The information propagation model with memory-clustering ability is designed with SIR model. And unsupervised learning is introduced to modify the performance. Based on the new network model, the core network and critical path that play a key role in the information propagation are found through the K-shell decomposition method. The research shows that the memory network contains an inertial channel for information propagation, it makes information propagation smooth. And information is selectively propagated in the new network, information is more inclined to propagate between nodes with powerful memory strength and close connections, in other words, people are more willing to propagate information to old friends who have been in contact for a long time instead of new friends.

scholarly journals Research of Social Network Information Propagation Model Based on Public Interest and Opinion

2016 ◽  
Vol 05 (02) ◽  
pp. 75-81 ◽  
Author(s):  
Juntao Li ◽  
Tingting Dong ◽  
Meng Li
Keyword(s):  
Social Network ◽  
Public Interest ◽  
Propagation Model ◽  
Information Propagation ◽  
Network Information ◽  
Model Based

Social network information propagation model based on individual behavior

2017 ◽  
Vol 14 (7) ◽  
pp. 1-15 ◽  
Author(s):  
Lejun Zhang ◽  
Hongjie Li ◽  
Chunhui Zhao ◽  
Xiaoying Lei
Keyword(s):  
Social Network ◽  
Individual Behavior ◽  
Propagation Model ◽  
Information Propagation ◽  
Network Information ◽  
Model Based

A Deep Adversarial Learning Methodology for Designing Microstructural Material Systems

2018 ◽  
Author(s):  
Xiaolin Li ◽  
Zijiang Yang ◽  
L. Catherine Brinson ◽  
Alok Choudhary ◽  
Ankit Agrawal ◽  
...  
Keyword(s):  
Latent Variables ◽  
Materials Design ◽  
Information Loss ◽  
Bayesian Optimization ◽  
Generative Adversarial Networks ◽  
Microstructural Characteristics ◽  
Significant Information ◽  
Adversarial Learning ◽  
Design Variables ◽  
Microstructural Design

In Computational Materials Design (CMD), it is well recognized that identifying key microstructure characteristics is crucial for determining material design variables. However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for materials design. Some MCR approaches are not applicable for material microstructural design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.

scholarly journals A Bio-Inspired Defensive Rumor Confinement Strategy in Online Social Networks

2021 ◽  
Vol 33 (1) ◽  
pp. 47-70
Author(s):  
Santhoshkumar Srinivasan ◽  
Dhinesh Babu L. D.
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Propagation Model ◽  
Information Propagation ◽  
Control Approach ◽  
Rumor Propagation ◽  
Immunization Strategies ◽  
New Information ◽  
The Individual ◽  
Rumor Control

Online social networks (OSNs) are used to connect people and propagate information around the globe. Along with information propagation, rumors also penetrate across the OSNs in a massive order. Controlling the rumor propagation is utmost important to reduce the damage it causes to society. Educating the individual participants of OSNs is one of the effective ways to control the rumor faster. To educate people in OSNs, this paper proposes a defensive rumor control approach that spreads anti-rumors by the inspiration from the immunization strategies of social insects. In this approach, a new information propagation model is defined to study the defensive nature of true information against rumors. Then, an anti-rumor propagation method with a set of influential spreaders is employed to defend against the rumor. The proposed approach is compared with the existing rumor containment approaches and the results indicate that the proposed approach works well in controlling the rumors.

Antinoise Learning and Coalitional Game GAN

2021 ◽  
Author(s):  
Hongyou Chen ◽  
Hongjie He ◽  
Fan Chen ◽  
Yiming Zhu
Keyword(s):  
Image Quality ◽  
Real Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Coalitional Game ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
Network Convergence ◽  
The Stability ◽  
Zero Sum

Adversarial learning stability has an important influence on the generated image quality and convergence process in generative adversarial networks (GANs). Training dataset (real data) noise and the balance of game players have an impact on adversarial learning stability. In the gradient backpropagation of the discriminator, the noise samples increase the gradient variance. It can increase the uncertainty in the network convergence progress and affect stability. In the two-player zero-sum game, the game ability of the generator and discriminator is unbalanced. Generally, the game ability of the generator is weaker than that of the discriminator, which affects the stability. To improve the stability, an antinoise learning and coalitional game generative adversarial network (ANL-CG GAN) is proposed, which achieves this goal through the following two strategies. (i) In the real data loss function of the discriminator, an effective antinoise learning method is designed, which can improve the gradient variance and network convergence uncertainty. (ii) In the zero-sum game, a generator coalitional game module is designed to enhance its game ability, which can improve the balance between the generator and discriminator via a coalitional game strategy. To verify the performance of this model, the generated results of the designed GAN and other GAN models in CELEBA and CIFAR10 are compared and analyzed. Experimental results show that the novel GAN can improve adversarial learning stability, generate image quality, and reduce the number of training epochs.

A two-layer SIR information propagation model with heterogeneity based on coupled network

2019 ◽  
Vol 76 (3) ◽  
pp. 1657-1679 ◽  
Author(s):  
Tongrang Fan ◽  
Wanting Qin ◽  
Wenbin Zhao ◽  
Feng Wu ◽  
Jianmin Wang
Keyword(s):  
Propagation Model ◽  
Information Propagation

scholarly journals Numerical analysis of an information propagation model

2019 ◽  
Vol 563 ◽  
pp. 052089
Author(s):  
Qin Wang ◽  
Xiao-li Wang
Keyword(s):  
Numerical Analysis ◽  
Propagation Model ◽  
Information Propagation
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