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

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.

Superconducting properties of Nb3(Sn1−xGax) by a solid‐state diffusion process

1976 ◽  
Vol 28 (12) ◽  
pp. 738-740 ◽  
Author(s):  
O. Horigami ◽  
Thomas Luhman ◽  
C. S. Pande ◽  
M. Suenaga
Keyword(s):  
Solid State ◽  
Diffusion Process ◽  
Solid State Diffusion ◽  
Superconducting Properties ◽  
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.

scholarly journals SUBSTITUTION OF TRANSITION METAL ION ON MAGNETIC BEHAVIOR of CA0.5SR0.5MEXFE12-2XO19 BY SOLID STATE DIFFUSION PROCESS

2015 ◽  
Author(s):  
M. N. Giriya, C. L. Khobaragade, D. S. Bhowmick, K. G. Rew M. N. Giriya, C. L. Khobaragade, D. S. Bhowmick, K. G. Rew ◽  
Keyword(s):  
Solid State ◽  
Transition Metal ◽  
Diffusion Process ◽  
Metal Ion ◽  
Magnetic Behavior ◽  
Solid State Diffusion ◽  
Transition Metal Ion ◽  
State Diffusion

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
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