scholarly journals Simulating the Information Diffusion Process in Complex Networks Using Push and Pull Strategies

2014 ◽  
Author(s):  
Dariusz Krol ◽  
Marcin Budka ◽  
Katarzyna Musial
Keyword(s):  
Diffusion Process ◽  
Complex Networks ◽  
Information Diffusion ◽  
Push And Pull

scholarly journals Influential Performance of Nodes Identified by Relative Entropy in Dynamic Networks

2020 ◽  
Vol 08 (01) ◽  
pp. 93-112
Author(s):  
Péter Marjai ◽  
Attila Kiss
Keyword(s):  
Diffusion Process ◽  
Complex Networks ◽  
Relative Entropy ◽  
Information Diffusion ◽  
Dynamic Networks ◽  
Centrality Measures ◽  
Topsis Method ◽  
Cover Time ◽  
Influential Nodes ◽  
Different Characteristics

For decades, centrality has been one of the most studied concepts in the case of complex networks. It addresses the problem of identification of the most influential nodes in the network. Despite the large number of the proposed methods for measuring centrality, each method takes different characteristics of the networks into account while identifying the “vital” nodes, and for the same reason, each has its advantages and drawbacks. To resolve this problem, the TOPSIS method combined with relative entropy can be used. Several of the already existing centrality measures have been developed to be effective in the case of static networks, however, there is an ever-increasing interest to determine crucial nodes in dynamic networks. In this paper, we are investigating the performance of a new method that identifies influential nodes based on relative entropy, in the case of dynamic networks. To classify the effectiveness, the Suspected-Infected model is used as an information diffusion process. We are investigating the average infection capacity of ranked nodes, the Time-Constrained Coverage as well as the Cover Time.

scholarly journals RNA: A Reject Neighbors Algorithm for Influence Maximization in Complex Networks

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1313
Author(s):  
Dongqi Wang ◽  
Jiarui Yan ◽  
Dongming Chen ◽  
Bo Fang ◽  
Xinyu Huang
Keyword(s):  
Diffusion Process ◽  
Complex Networks ◽  
Information Diffusion ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Network Centrality ◽  
Diffusion Effect ◽  
Node Ranking ◽  
Key Nodes ◽  
Influence Maximization Problem

The influence maximization problem (IMP) in complex networks is to address finding a set of key nodes that play vital roles in the information diffusion process, and when these nodes are employed as ”seed nodes”, the diffusion effect is maximized. First, this paper presents a refined network centrality measure, a refined shell (RS) index for node ranking, and then proposes an algorithm for identifying key node sets, namely the reject neighbors algorithm (RNA), which consists of two main sequential parts, i.e., node ranking and node selection. The RNA refuses to select multiple-order neighbors of the seed nodes, scatters the selected nodes from each other, and results in the maximum influence of the identified node set on the whole network. Experimental results on real-world network datasets show that the key node set identified by the RNA exhibits significant propagation capability.

scholarly journals Dynamics of information diffusion and its applications on complex networks

2016 ◽  
Vol 651 ◽  
pp. 1-34 ◽  
Author(s):  
Zi-Ke Zhang ◽  
Chuang Liu ◽  
Xiu-Xiu Zhan ◽  
Xin Lu ◽  
Chu-Xu Zhang ◽  
...  
Keyword(s):  
Complex Networks ◽  
Information Diffusion

scholarly journals An Efficient Partition-Based Approach to Identify and Scatter Multiple Relevant Spreaders in Complex Networks

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1216
Author(s):  
Jedidiah Yanez-Sierra ◽  
Arturo Diaz-Perez ◽  
Victor Sosa-Sosa
Keyword(s):  
Complex Networks ◽  
Information Diffusion ◽  
Large Scale ◽  
Centrality Measures ◽  
Correct Identification ◽  
Graph Analysis ◽  
Relevant Role ◽  
Two Phases ◽  
Large Scale Networks ◽  
Spreading Processes

One of the main problems in graph analysis is the correct identification of relevant nodes for spreading processes. Spreaders are crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases, rumors, and more. Correct identification of spreaders in graph analysis is a relevant task to optimally use the network structure and ensure a more efficient flow of information. Additionally, network topology has proven to play a relevant role in the spreading processes. In this sense, more of the existing methods based on local, global, or hybrid centrality measures only select relevant nodes based on their ranking values, but they do not intentionally focus on their distribution on the graph. In this paper, we propose a simple yet effective method that takes advantage of the underlying graph topology to guarantee that the selected nodes are not only relevant but also well-scattered. Our proposal also suggests how to define the number of spreaders to select. The approach is composed of two phases: first, graph partitioning; and second, identification and distribution of relevant nodes. We have tested our approach by applying the SIR spreading model over nine real complex networks. The experimental results showed more influential and scattered values for the set of relevant nodes identified by our approach than several reference algorithms, including degree, closeness, Betweenness, VoteRank, HybridRank, and IKS. The results further showed an improvement in the propagation influence value when combining our distribution strategy with classical metrics, such as degree, outperforming computationally more complex strategies. Moreover, our proposal shows a good computational complexity and can be applied to large-scale networks.

scholarly journals Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction

2019 ◽  
Vol 9 (18) ◽  
pp. 3758 ◽  
Author(s):  
Xiang Li ◽  
Xiaojie Wang ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi
Keyword(s):  
Complex Networks ◽  
Source Localization ◽  
Information Diffusion ◽  
Large Scale ◽  
Computational Cost ◽  
Likelihood Estimation ◽  
Power Grids ◽  
Optimization Approach ◽  
Localization Accuracy ◽  
Diffusion Source

Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.

The rumor diffusion process with emerging independent spreaders in complex networks

2014 ◽  
Vol 397 ◽  
pp. 121-128 ◽  
Author(s):  
Weihua Li ◽  
Shaoting Tang ◽  
Sen Pei ◽  
Shu Yan ◽  
Shijin Jiang ◽  
...  
Keyword(s):  
Diffusion Process ◽  
Complex Networks

scholarly journals An epidemic model for correlated information diffusion in crowd intelligence networks

2019 ◽  
Vol 3 (2) ◽  
pp. 168-183 ◽  
Author(s):  
Yuejiang Li ◽  
H. Vicky Zhao ◽  
Yan Chen
Keyword(s):  
Diffusion Process ◽  
Mixture Model ◽  
Information Diffusion ◽  
Stable State ◽  
Stable Condition ◽  
Real Data ◽  
Information Flows ◽  
Content Type ◽  
Correlated Information ◽  
Crowd Intelligence

Purpose With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks. Design/methodology/approach This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them. Findings The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model. Originality/value The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

scholarly journals LEARNING FROM THE EXPECTATIONS OF OTHERS

2008 ◽  
Vol 12 (3) ◽  
pp. 345-377 ◽  
Author(s):  
JIM GRANATO ◽  
ERAN A. GUSE ◽  
M. C. SUNNY WONG
Keyword(s):  
Asymmetric Information ◽  
Diffusion Process ◽  
Adaptive Learning ◽  
Information Diffusion ◽  
Multiple Equilibria ◽  
Forecast Error ◽  
Mean Square ◽  
Cobweb Model ◽  
Boomerang Effect ◽  
Forecast Efficiency

This paper explores the equilibrium properties of boundedly rational heterogeneous agents under adaptive learning. In a modified cobweb model with a Stackelberg framework, there is an asymmetric information diffusion process from leading to following firms. It turns out that the conditions for at least one learnable equilibrium are similar to those under homogeneous expectations. However, the introduction of information diffusion leads to the possibility of multiple equilibria and can expand the parameter space of potential learnable equilibria. In addition, the inability to correctly interpret expectations will cause a “boomerang effect” on the forecasts and forecast efficiency of the leading firms. The leading firms' mean square forecast error can be larger than that of following firms if the proportion of following firms is sufficiently large.

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