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 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.

Modelling multi-state diffusion process in complex networks: theory and applications

2014 ◽  
Vol 2 (4) ◽  
pp. 431-459 ◽  
Author(s):  
Y. Lin ◽  
J. C. S. Lui ◽  
K. Jung ◽  
S. Lim
Keyword(s):  
Diffusion Process ◽  
Complex Networks ◽  
State Diffusion

scholarly journals TIME CENTRALITY IN DYNAMIC COMPLEX NETWORKS

2015 ◽  
Vol 18 (07n08) ◽  
pp. 1550023 ◽  
Author(s):  
EDUARDO C. COSTA ◽  
ALEX B. VIEIRA ◽  
KLAUS WEHMUTH ◽  
ARTUR ZIVIANI ◽  
ANA PAULA COUTO DA SILVA
Keyword(s):  
Diffusion Process ◽  
Complex Networks ◽  
Diffusion Processes ◽  
Temporal Evolution ◽  
Dynamic Network ◽  
Time Varying ◽  
Relative Importance ◽  
Node Centrality ◽  
Centrality Metrics ◽  
Efficient Diffusion

There is an ever-increasing interest in investigating dynamics in time-varying graphs (TVGs). Nevertheless, so far, the notion of centrality in TVG scenarios usually refers to metrics that assess the relative importance of nodes along the temporal evolution of the dynamic complex network. For some TVG scenarios, however, more important than identifying the central nodes under a given node centrality definition is identifying the key time instants for taking certain actions. In this paper, we thus introduce and investigate the notion of time centrality in TVGs. Analogously to node centrality, time centrality evaluates the relative importance of time instants in dynamic complex networks. In this context, we present two time centrality metrics related to diffusion processes. We evaluate the two defined metrics using both a real-world dataset representing an in-person contact dynamic network and a synthetically generated randomized TVG. We validate the concept of time centrality showing that diffusion starting at the best ranked time instants (i.e., the most central ones), according to our metrics, can perform a faster and more efficient diffusion process.

The SIS diffusion process in complex networks with independent spreaders

2020 ◽  
Vol 546 ◽  
pp. 122921 ◽  
Author(s):  
Qin Ding ◽  
Weihua Li ◽  
Xiangming Hu ◽  
Zhiming Zheng ◽  
Shaoting Tang
Keyword(s):  
Diffusion Process ◽  
Complex Networks

Information entropy of diffusion processes on complex networks

2014 ◽  
Vol 28 (17) ◽  
pp. 1450141 ◽  
Author(s):  
Zhanli Zhang
Keyword(s):  
Diffusion Process ◽  
Complex Networks ◽  
Information Entropy ◽  
Diffusion Processes ◽  
Structural Characteristics ◽  
Optimal Routing ◽  
Information Propagation ◽  
Scale Free ◽  
Routing Strategy ◽  
Computer Scientists

Diffusion processes have been widely investigated to understand some essential features of complex networks, and have attracted much attention from physicists, statisticians and computer scientists. In order to understand the evolution of the diffusion process and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the information entropy comprehending the structural characteristics and information propagation on the network. Based on the analysis of the diffusion process, we analyze the coupling impact of the structural factor and information propagating factor on the information entropy, where the analytical results fit well with the numerical ones on scale-free complex networks. The information entropy can better characterize the complex behaviors on networks and provides a new way to deepen the understanding of the diffusion process.

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 Network entropy using edge-based information functionals

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Furqan Aziz ◽  
Edwin R Hancock ◽  
Richard C Wilson
Keyword(s):  
Diffusion Process ◽  
Complex Networks ◽  
Structural Information ◽  
Structural Complexity ◽  
Network Models ◽  
Quantum Graph ◽  
Heat Diffusion ◽  
Graph Representation ◽  
Edge Based ◽  
Local Edge

Abstract In this article, we present a novel approach to analyse the structure of complex networks represented by a quantum graph. A quantum graph is a metric graph with a differential operator (including the edge-based Laplacian) acting on functions defined on the edges of the graph. Every edge of the graph has a length interval assigned to it. The structural information contents are measured using graph entropy which has been proved useful to analyse and compare the structure of complex networks. Our definition of graph entropy is based on local edge functionals. These edge functionals are obtained by a diffusion process defined using the edge-based Laplacian of the graph using the quantum graph representation. We first present the general framework to define graph entropy using heat diffusion process and discuss some of its properties for different types of network models. Second, we propose a novel signature to gauge the structural complexity of the network and apply the proposed method to different datasets.

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